Harvard (Harvard Medical School) | Department of Cardiology

In this research course, we will talk about the principles of developmental biology and stem cell biology, study organ development (such as the heart), and discuss genome engineering using the CRISPR-Cas9 (a novel genetic modification tool). The objective of the research course is to encourage students to think creatively about how a cell develops an organism, how we can study them experimentally, how we can edit genes, and how we can use animal models to analyse in vivo data.

Brown | BioMed MCB Department

In this research course, each student is tasked to read, digest, and present assigned peer-reviewed research articles in the field of Cell and Developmental Biology, which is the science that investigates how interacting processes generate an organism’s heterogeneous shapes, size, and structural features that arise on the trajectory throughout a life cycle. Topics of interest include asymmetric cell division, cell signalling and metabolism, cellular specification and differentiation, mRNA translation, embryonic development, germ cell and stem cell development, and cancer regulation.

Cambridge | Cambridge Institute for Medical Research

Injury to the brain and spinal cord has devastating consequences as adult neurons in the central nervous system do not repair their nerve fibres. This research course covers the cellular and molecular biology of the nervous system and has a particular focus on the regrowth of nerve fibres after an acute injury – a process called axon regeneration. The independent research project will enable students to gain hands-on experience in the analysis of real experimental data from fluorescence microscopy of neurons and the presentation of scientific results.

Cambridge | Cambridge Institute for Medical Research

As technology advances and scientists pose increasingly complex questions, much of the current research focuses on the intricate relationship between genes, proteins, and their role in biological processes. This research course provides students with hands-on experience using computational tools to predict and analyze the effects of mutations. Students will learn how computational approaches can complement experimental research and drive innovation in drug discovery and disease prevention.

Princeton | Department of Molecular Biology

In this course, students will receive a comprehensive introduction to the fascinating world of protein biochemistry. We will span several decades of technological advancements including the polymerase chain reaction (PCR), recombinant protein expression technologies, CRISPR/Cas9 gene editing, and advances in optical microscopy approaches. We will combine fundamental concepts in molecular biology and biochemistry with the seminal literature in the field that has led to major scientific breakthroughs and altered the way textbooks are written. Students will explore protein-based machines through independent research projects using basic molecular biology and biochemical techniques (if available) and/or computational AI-based tools.

Oxford | Department of Biochemistry

In this research course, we will explore genetics utilising computational and data scientific techniques. By analysing the vast quantities of data using these cutting-edge techniques, students will learn how to observe and analyze molecular events across the genomes of biological systems.

Cambridge | Department of Genetics

In this research course, students will learn about the role of
genetics in understanding and combatting infectious
diseases, while developing skills relevant in modern computational analyses, known as bioinformatics, which will open
the door for understanding infection mechanisms at the
gene and protein levels.

Cambridge | Department of Medicine

Pandemics are a major threat to public health and can have severe consequences for global economies, making it important to study the factors that drive their emergence and spread. This research course will delve into the fascinating and complex world of infectious diseases from a genetic perspective. It  will focus on the use of whole-genome genetic data to better understand the molecular mechanisms underlying the spread and evolution of pathogens that cause infectious diseases. 

Cambridge | Department of Pharmacology

In this course, we will learn the basic principles of cancer biology and how to use different bioinformatics tools to analyse health data. We will discuss the hallmarks of cancer to better understand how we can interpret the results we can discover from the data-mining exercises. We will explore large-scale biological data using different bioinformatics tools and platforms. These findings will allow students to uncover genes that can have a potential role as biomarkers or that have therapeutic applications.

Cambridge | School of Clinical Medicine

In today’s era of data-intensive biology, the analysis of biomedical data, from multi-omics and imaging to genomics and phenotypic profiles, is essential for unraveling the complexities of human health and disease. By the end of the research course, students will be equipped with a robust understanding of both biological data analysis and the AI-driven tools that are reshaping today’s biomedical research.

Cambridge | Department of Pharmacology

In this course, we will learn the basic principles of genomics and molecular profiling approaches used to analyse an individual’s genetic information. We will also engage in hands-on research by utilising bioinformatics tools to identify biomarkers—specific indicators associated with particular diseases or treatment responses. As precision medicine involves integrating data from various omics sources, such as genomics, transcriptomics, proteomics, and metabolomics, we will use bioinformatics tools to explore and integrate multi-dimensional data, offering a more comprehensive view of biological processes. The potential role of artificial intelligence, machine learning and other innovative approaches in shaping the future of precision medicine will also be discussed.

UC Berkeley | Landry Lab, Department of Chemical and Biomolecular Engineering

This research course will cover emerging topics in applied biotechnology – from CRISPR to cloning. We will learn the fundamental principles of DNA, RNA, and protein biochemistry and think about how analogous techniques to study and analyse these systems have emerged. Next, we will discuss the development of CRISPR-based genome editing applications. The scope of the research course will allow students to probe the cutting-edge interface of biology with engineering.

UC Berkeley | Landry Lab, Department of Chemical and Biomolecular Engineering

This research course will focus on technologies to monitor and manipulate the brain and the nervous system, including implantable medical devices, fundamental neurobiology, and brain-machine interface applications. This comprehensive research course will tackle technologies leveraged to treat neuropsychiatric and neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease, schizophrenia, depression, anxiety, and social autism spectrum disorders.

Cambridge | Cancer Research UK Cambridge Institute

This research course aims to explain how genetic variations in our genome affect our phenotype and how genetic variations lead to single gene and complex diseases such as cancer. Most importantly we will explore modern cancer diagnostics and novel methods for early cancer detection and how clinicians nowadays use patients’ DNA to target and treat cancer — offering a new approach to personalised treatment. 

Harvard | Joslin Diabetes Center

Throughout the research course, students will explore cutting-edge strategies and technologies used in drug discovery with a focus on metabolism-targeting therapeutics. The rapidly advancing field of drug development offers various approaches, including novel drug design, high-throughput screening methods, and precision medicine techniques. By exposing students to these state-of-the-art methodologies, the course aims to equip them with the knowledge and skills necessary to engage in contemporary research efforts and contribute to the development of next-generation drugs aimed at combating metabolic diseases.

Cambridge | Biomineral Research Lab

Biological imaging methods serve as indispensable tools for illuminating the dynamics of biological systems, offering insights into their inner workings at the cellular and molecular levels. This research course acts as a gateway into the realm of bio-image processing techniques, empowering students with the necessary knowledge and skills to dissect, analyse, and interpret biological images with precision and efficacy. The aim of the course is to cultivate both a theoretical and practical understanding of image processing methods meticulously tailored to meet the unique challenges posed by biological data.

Harvard | Massachusetts General Hospital 

Regenerative medicine, using biologically compatible materials (biomaterials) and stem cells, aims to restore the functions of damaged organs or tissues. We will examine the basics of stem cell biology, explore different types of biomaterials, discuss current tissue engineering strategies and commercially available tissue engineering products. Ultimately, students will each conduct a research project in which participants can develop their own hypothetical
tissue engineering strategies to restore a type of tissue of their own choosing.

Cambridge | Department of Veterinary Medicine

This course offers an introduction to bacterial genomics within the context of the pressing global concern—antimicrobial resistance (AMR). Students delve into resistance genes, horizontal gene transfer, and cutting-edge DNA sequencing techniques, gaining practical skills in bioinformatics for AMR surveillance. The course goes beyond theoretical understanding, exploring strategies to combat AMR, including responsible antibiotic use, alternative therapies, and global initiatives. Emphasis is placed on hands-on exercises, discussions, and collaborative projects that empower students to analyse real-world genomic data and propose solutions to address AMR challenges.

Princeton | Molecular Biology Department

Analytical chemistry plays a crucial role in modern science, particularly in areas such as disease biomarker detection, biosensor development, and gene-editing technologies. In this research course, students will explore the fundamental concepts of disease biomarkers and their applications in disease diagnosis, delving into how chemical analysis techniques achieve high sensitivity and specificity in detection. The research course also focuses on the principles and design of biosensors, including the integration of nanomaterials and chemical modification techniques to enhance sensing performance.

Cambridge | Department of Engineering 

Our brain controls how we perceive our surroundings and
how we interact with them, how we feel, and who we are. In
this research course, we will explore how the brain and
nervous system function with a particular attention to its
core building block – the neuron. We will also explore how
technology can be utilised to better understand and treat
the brain.

Cambridge/Johns Hopkins | Bioelectronics Laboratory

The objective of this research course is to invite students to study neuroscience and neuroanatomy, and to understand the state-of-the-art technology being used to interface with the nervous system. In particular, we will learn the physiological basis of electrical and chemical signalling in the nervous system, including the brain and the sensory systems (visual, auditory, olfactory and taste, and hearing), and understand how electronic systems can be used to artificially substitute them when damaged (e.g. recovery of hearing in deaf people).

University of Toronto, Keenan Research Centre for Biomedical Sciences | St Michael’s Hospital

This research course offers an engaging introduction to psychology’s fundamental concepts and principles. Students will be provided with an overview of the scientific study of human behaviour and thought by exploring topics such as perception, attention, memory, motivation, and decision-making. Particular focus is placed on the emotions. We will discuss the evolutionary origins of distinct emotions, as well as the impact of emotions on our cognitive processes and social relationships. Students will also be trained to discuss and present on research data and clinical experiences to enhance their understanding.

Oxford | Department of Experimental Psychology

This research course will examine the building blocks of human intelligence through the lenses of cognitive psychology. We will explore the fundamental cognitive processes such as attention, memory, and learning. We will then examine how these core abilities give rise to more complex processes such as decision making, problem solving and abstract thinking, and ultimately to what we consider intelligent behaviour. The overall aim of the course is to provide students with a thorough understanding of the key topics in cognitive psychology, to provide a space to integrate theoretical and experimental knowledge, and equip students with a thorough understanding of the tools and approaches used to study cognition.

Harvard (Harvard Medical School) | Beth Israel Deaconess Medical Center, Boston

The blood-brain barrier is a major field in neuroscience, because it protects the brain from harmful substances in the blood while allowing essential nutrients to pass through. In this research course, we will grasp the essential concepts in cellular and molecular medicine, neuroscience, and genetics, specifically focusing on the blood-brain barrier (BBB) and its critical role in brain function.

Harvard (Harvard Medical School) | Beth Israel Deaconess Medical Center, Boston

This research course offers a comprehensive introduction to the foundational principles of stem cell biology and the emerging and transformative field of regenerative medicine. It is designed for students seeking to understand the dynamic role of stem cells in both biological research and clinical applications. This foundation will equip students with the knowledge needed to pursue further studies or a career in stem cell science and regenerative medicine, fostering their contribution to one of the most promising and rapidly evolving areas of modern biology.

Oxford | Department of Experimental Psychology

Our visual sense is one of the most important means of gathering information about the surrounding physical world. In this research course, we will examine the core topics in visual perception, which form a major part in experimental psychology, cognitive science, and optometry. Students will obtain a foundational understanding of the principles, theories, and processes involved in visual perception, spanning from the basic functions of the eye to the complexities of visual cognition.

Cambridge | Department of Clinical Neurosciences

Many of us will be personally affected by dementia by either getting dementia ourselves or caring for someone with dementia. The research course will introduce students to dementia and dementia research. We will cover different types of dementias, including Alzheimer’s, Vascular dementia, frontotemporal dementia, Parkinson’s, and HIV-associated brain injury, and more. Through them we will gain an understanding of how brain diseases influence cognition, emotion, and behaviour. We will also study dementia prevention, in which we will look at evidences of how our own lifestyle choice could affect getting dementia.

Dartmouth | Computational and Cognitive Neuroscience Lab, Department of Psychological and Brain Sciences

In this research course, we will examine decision making from both behavioural and neurobiological points of view. Specifically, we will learn about different methods used in psychology and neuroscience to study decision making at various levels, from mental and cognitive processes to underpinning neural activity and mechanisms. Ultimately, this research course will alter students’ perspectives on decision-making by imparting knowledge of brain function.

Dartmouth | Computational and Cognitive Neuroscience Lab, Department of Psychological and Brain Sciences

Neuroeconomics is a new emerging field in which a combination of methods from neuroscience, psychology, and economics is used to better understand how we make decisions. Neuroeconomics uses various cutting edge techniques to study how the brain integrates information from various sources. In this research course, we learn about economic and psychological theories that are used to investigate and understand choice behaviour, as well as mental and neural processes that underlie decision-making.

Oxford | Department of Experimental Psychology

This research course delves into the fascinating realm of how the human brain supports learning and decision-making processes, drawing insights from computational neuroscience. Throughout the course, we will explore fundamental concepts such as reinforcement learning and Bayesian decision theory, unravelling the intricate mechanisms that underlie our cognitive abilities. By synthesising insights from neuroscience, psychology, and computer science, students will develop a holistic understanding of human learning and decision-making processes. In summary, this course offers a multidisciplinary perspective that will deepen students’ understanding of the complex interplay between the brain, behaviour, and computational principles.

Cambridge | Department of Psychology 

Motivation and emotion are critical functions of the brain,
allowing individuals to enhance their likelihood of survival
and passing on their genes. In this research course, we will
aim to provide a foundation of research, theory and
practical skills acting as a primer for the student interested
in the psychological and neural basis of emotion, motivated behaviours and the mechanisms of abnormal emotion
and motivation.

Cambridge | Department of Psychology 

In this research course, we will explore foundational research, theory and practical skills related to molecular and systems pharmacology of central nervous system disorders. The course will provide the students with a solid background in cellular and molecular neuroscience, neuropharmacology and behavioural neuroscience that will then be used to discuss the neuropsychopharmacology of neuropsychiatric disorders. The aim of this research course is to provide an understanding of the chemical pathology of the major central nervous system diseases/disorders, and how these conditions are treated with drugs.

Harvard (Harvard Medical School) | Dettmer Lab, Department of Neurology

In this research course, students will deepen their under- standing of brain anatomy, brain biology and the degeneration that occurs in Parkinson’s disease and causes the hallmarks of PD. Potential intervention strategies will be evaluated. The importance of biomarkers for diagnosis and drug development will be discussed, and potential biomarker strategies will be highlighted. The goal is to outline novel strategies towards (early) diagnosis and treatment of PD, and this may include the combination of different approaches.

Harvard, Center for Astrophysics | NASA Jet Propulsion Laboratory (JPL)

In this research course, students will generate a scientific exploration case, develop the mission concept, as well as design and investigate custom subsystems of a spacecraft, such as structures, thermal, power, attitude and orbit and propulsion. Students will study celestial mechanics/astrodynamics in order to determine the most suitable orbits in space and how this affects key engineering considerations. This course is well suited to students with an interest across space research, astronomy, aerospace engineering, and mechanical engineering.

Oxford | Department of Engineering Science

This research course provides a comprehensive exploration of the essential engineering principles of thermofluids, aerodynamics, and experimental design, equipping students with the theoretical knowledge and practical skills needed to address challenges in aerospace, energy, and sustainable technologies. Students will be prepared to apply these skills to innovate and create solutions for achieving sustainability and net-zero objectives in the industries of aerospace, sustainable energy, industrial systems, and beyond.

University College London 

DNA molecules have particular chemical and physical properties that can be applied to solve tasks that go beyond the scope of their function in nature. In this research course, we will explore first DNA’s functional characteristics and how can they be used to produce complex architectures at the nanoscale that can then perform customised tasks for a wide range of applications – from biomedicine to the manufacturing industry, including data storage and complex chemical production.

UCL | Department of Computer Science

Biorobotics is a cutting-edge interdisciplinary science at the intersection of biology, biomedical engineering, computer science and robotics. It studies ways to improve the intelligence, locomotion, and other performances of robotic systems inspired by nature. In this research course, students will be introduced to novel bio-inspired ideas that have revolutionised modern day robotics, particularly in the field of soft-robotics. The course delves into the principles and methods behind the design of physically compliant robots. Students will learn the programming language MATLAB and develop their independent research projects on bio-inspired robotics.

Cambridge | Bio-Inspired Robotics Lab

This research course introduces students to the intersection of robotics and artificial intelligence, with a focus on the machine learning techniques that power advancements in modern robotics. Covering a broad spectrum of intelligence levels, the research course begins with foundational concepts, such as understanding touch and interaction with the physical environment, and progresses to advanced methodologies, including reinforcement learning, learning from human demonstrations, and multi-agent collaboration.

UCL | Department of Medical Physics and Biomedical Engineering

Soft robotics is a rising branch of robotics that aims to develop delicate, flexible and safe robotic devices which interact with humans using soft actuators that mimic biological behaviour, which state of the art rigid robots cannot accomplish otherwise. In practice, they can perform tasks that would be impossible or dangerous for humans to do. This research course will introduce students to this nascent branch of robotics and have a deeper insight into soft robots’ concept, development, and control. With this, the students will develop a full awareness of the topics, which will allow them to work on their independent research projects.

Oxford | Department of Physics

This is an interdisciplinary research course at the interface of microengineering, analytical chemistry, and robotics, designed to explore the innovative applications of miniaturized analytical systems and automated chemical processes. Students will learn cutting-edge techniques in microfluidics and robotics and apply them to real-world problems in analytical chemistry and biomedical engineering.

UC Berkeley | College of Engineering 

This research course provides preparation for the conceptual design and prototyping of mechanical systems that use microprocessors to control machine activities, acquire and analyse data, and interact with operators. Students will perform laboratory exercises that lead through studies of different levels of software. Software coverage includes C and Matlab. Students will have the opportunity to work with an Infineon PSOC6 microcontroller.

Oxford | Department of Engineering

In this research course, we will explore the fundamental principles of mechanics in this comprehensive course that covers the essential and advanced concepts of statics and dynamics. Throughout the research course, hands-on exercises, problem-solving sessions, and interactive simulations will allow students to apply theoretical concepts to practical situations. By the end of this course, students will possess a solid understanding of basic mechanics, enabling them to analyse static equilibrium, assess structural members, and predict the behaviour of particles and rigid bodies in dynamic situations.

Cambridge | Department of Medicine / Department of Engineering

Nanotechnology is a multidisciplinary field that draws from physics, chemistry, biology, and engineering. It is a rapidly evolving field that offers novel solutions for many industrial challenges. In this research course, students will learn about various aspects of nanotechnology and nanomaterials, and how they are applied to create devices such as solar cells, superconductors, and medical sensors.

Cambridge | Department of Medicine / Department of Engineering

This research course covers the entire process of sensor data science: data collection, pre-processing, feature extraction, and machine learning modelling. Mobile and wearable sensors will be mainly used, and the types of sensor data covered include motion (e.g. vibration/acceleration, GPS), physiological signals (e.g. heart rate, skin temperature), and interaction data (e.g. app usage). Students will learn the basic digital signal processing and feature extraction techniques. Basic machine learning techniques will be reviewed, and students will master these techniques with a final mini-project to solve real-world sensor data science problems.

MIT | Department of Mathematics

Geometry, the study of shapes, is a fundamental aspect of both mathematics and our understanding of the world around us. In this advanced research course, we will embark on an exploration of the beautiful and intricate structures that define our universe. By the end of the course, students will have developed a strong foundation in modern differential geometry and be guided to write a paper proving a geometric theorem on their own, providing them with a taste of conducting research in pure mathematics.

UPenn | Department of Mathematics

The quadratic reciprocity law, conjectured by Euler and elegantly proved by Gauss, is one of the most profound theorems in mathematics. Its significance extends beyond its initial discovery, laying the groundwork for modern algebraic number theory and inspiring countless generalizations. This research course is designed to introduce students to the foundational concepts of number theory through the lens of quadratic residues and reciprocity. The research-oriented approach aims to equip students with the analytical skills needed to explore open problems in mathematics.

UPenn | Department of Mathematics

Linear algebra is a cornerstone of modern pure and applied mathematics, as well as theoretical computer science, with widespread applications in physics, engineering, economics, and data science. This research course provides a rigorous introduction to its fundamental concepts, including vector spaces, basis and dimension, quotient spaces, linear maps and matrices, determinants, dual spaces and maps, eigenvalues, invariant subspaces, and scalar products in Euclidean, unitary, and symplectic spaces.

UPenn | Department of Mathematics

This research course provides a comprehensive introduction to modern algebra, laying the foundation for further study in abstract algebra and related mathematical disciplines. It covers fundamental topics in group theory and ring theory, both of which play a crucial role in understanding algebraic structures and their applications in various mathematical fields. By the end of the course, students will have a strong foundation in modern algebra, preparing them for advanced studies in mathematics, theoretical computer science, cryptography, and related fields.

Cambridge | Faculty of Philosophy 

This research course will delve into the intricate relationship between mathematics and philosophy. Students will explore topics such as mathematical logic, set theory, and computability theory. The aim of this course is to inspire students to engage in independent research projects focusing on fundamental questions in mathematics, logic, and philosophy. Through this exploration, students will gain a fresh perspective on mathematics and its connection to broader philosophical inquiries.

Cambridge | Department of Computer Science and Technology

This research course will delve into the intricate relationship between mathematics and philosophy. Students will explore topics such as mathematical logic, set theory, and computability theory. The aim of this course is to inspire students to engage in independent research projects focusing on fundamental questions in mathematics, logic, and philosophy. Through this exploration, students will gain a fresh perspective on mathematics and its connection to broader philosophical inquiries.

Cambridge | Department of Computer Science and Technology

For millennia, humanity has pondered the nature of reasoning and whether it can be governed by clear rules. The quest for these “simple enough” rules, rooted in basic principles yet powerful enough to encompass various processes, spurred the development of mathematical logic and theoretical models of computation. This research course explores this historical journey, from Euclid’s axioms to the works of Frege, Peano, Russell, Gödel, and Turing, delving into formal systems, Gödel’s incompleteness theorems, and computational models like recursive functions and Turing machines. Bridging theory and practice, students explore automated reasoning and interactive theorem proving, gaining insight into the potential and limitations of formal systems, thus equipping them with tools for their development and application.

National Institutes of Health (NIH) | National Cancer Institute

Machine learning has emerged as a powerful tool across various industries, revolutionising how we approach complex problems. At its core, machine learning relies heavily on mathematical principles and techniques to make sense of data and make informed decisions. In this research course, students will be introduced to many concepts in advanced mathematics, from linear algebra, to derivatives, gradients, optimisation theory and information theory. By the end of the research course, students will gain a solid understanding of the mathematical principles that drive machine learning algorithms, equipping them with the knowledge and skills needed to tackle complex problems in the field.

Harvard, Center for Astrophysics | NASA Jet Propulsion Laboratory (JPL)

This research course is designed for students seeking a strong mathematical foundation to utilize mathematical tools to solve research problems in physics and engineering topics. Students will explore topics such as vector calculus, linear algebra, complex numbers, differential equations, and Fourier analysis – core mathematical techniques essential for understanding classical mechanics, electromagnetism, quantum physics, and engineering principles.

Harvard, Center for Astrophysics | NASA Jet Propulsion Laboratory (JPL)

In this research course, we will cover the fundamentals of machine learning as well as study how to develop code that can be applied to engineering system design. This research course will allow students to hone their coding skills, predominantly using Python, in order to perform linear regressions, data analytics, Bayesian optimizations, and multi-parameter analyses for engineering design cases. Having the ability to program and code in Python is an increasingly vital skill for all engineers. This course will be of interest to students interested in bioengineering, mechanical engineering, aerospace engineering, with a specific focus on using machine learning and computer vision tools.

Cambridge | Department of Computer Science and Technology

Natural language processing (NLP) is a field of artificial
intelligence that has seen significant development in recent years with current applications include virtual assistants, language translation tools, and sentiment analysis for social media. In this research course, students will learn about the fundamental techniques for processing language for several subtasks, such as morphological processing, parsing, word sense disambiguation, etc. They will also have the opportunity to explore the various applications of these techniques in real-world scenarios.

Cambridge | Department of Computer Science and Technology

In this research course, we will first systematically introduce the fundamental tasks and typical applications of NLP, such as sentiment analysis, machine translation, and text summarisation. We will then introduce pre-training technologies, such as masked language modeling and autoregressive language modelling, and representative pre-trained language models (PLMs), including BERT and GPT, along with their architectures. Building on these, we will discuss how these PLMs scale up to LLMs. On this basis, students will conduct a hands-on research project to apply LLMs to real-world tasks and critically examine their strengths and limitations.

Cambridge | Language Technology Lab 

In this research course, we will explore key concepts in Deep Learning and Natural Language Processing. Hands-on components will let the students build and train deep learning models, fine-tune advanced language models like GPTs. This research course offers a transformative experience, gearing the students up for future academic and professional pursuits in AI.

Cambridge | School of Clinical Medicine

The focus of this research course is learning end-to-end
models for these tasks, particularly image classification and
segmentation, using machine learning architectures. During
this course, students will gain a detailed understanding of
cutting-edge research in the fields of artificial intelligence,
computer vision, and artificial neural networks. Additionally,
the final assignment will allow them to apply their hands-on
knowledge to real-world vision problems.

Oxford | Department of Engineering Science

This research course provides a comprehensive introduction to deep learning techniques tailored for computer vision and medical image analysis. Through a combination of theoretical discussions and hands-on projects, students will explore techniques for image classification, segmentation, object detection, and anomaly detection. By the end, students will have able to design, train, and evaluate state-of-the-art models for a variety of computer vision tasks.

Oxford | Department of Engineering Science

This research course delves into the cutting-edge advancements in artificial intelligence, with a particular focus on the latest trends, i.e, foundation models and multimodal large language models (LLMs), such as GPT, BERT, and DALL-E. The research course examines the architectures and training paradigms underpinning these models, including self-supervised learning, attention mechanisms, and scaling laws.

Cambridge | School of Clinical Medicine

This research course introduces students to large-scale language models like GPT, exploring how machines comprehend and generate human-like text. It blends theory with practical exercises in natural language processing, aiming to demystify AI and inspire further study and careers in technology. Beginning with intensive introductions to machine learning and neural networks, the course progresses to independent research projects supervised by faculty. Students delve into advanced NLP techniques and are prompted to consider the ethical implications and boundaries of AI.

Imperial College London | Department of Computing

This research course delves into the realm of Natural Language Processing (NLP) and Large Language Models (LLMs), such as ChatGPT, which have revolutionised various domains but are prone to errors with significant implications. It aims to equip students with NLP tools to analyse, understand, and mitigate LLM errors. Beginning with NLP basics, the course explores LLM principles, architecture, and training methods, fostering hands-on experience with state-of-the-art tools for error analysis. Students engage in ethical discussions on AI deployment and decision-making, emphasising accountability and fairness. Through independent research projects, students investigate LLM errors, developing critical thinking and problem-solving skills essential for responsible AI development.

University of California | School of Information

The proliferation of smart spaces, ranging from intelligent homes and offices to advanced urban environments, has resulted in an explosion of video data generated by connected devices such as surveillance cameras, drones, and IoT-enabled sensors. Harnessing this data effectively requires a unique intersection of expertise in data science, distributed systems, and video analytics. This course aims to equip students with the knowledge and skills to design, implement, and optimize systems for processing and analyzing video data in smart spaces.

University of California | School of Information

This research course offers an in-depth exploration of data science, blending foundational principles with hands-on applications to equip students with the skills needed for real-world challenges. We will also delve into large language models (LLMs), such as GPT, and their transformative role in modern data science. Through practical projects, students will have a comprehensive understanding of the data science pipeline, from data preparation to advanced machine learning and AI integration.

Cambridge | Centre for Advanced Research and Education

Artificial Intelligence and Machine Learning are transforming industries and driving innovation across diverse fields, from healthcare to finance to environmental sustainability. This research course explores real-world applications of AI and ML, showcasing how these tools are used to address critical challenges, including vaccine development in the case of Moderna, text mining for stock market prediction, to computer vision for autonomous robots, and many more.

UCLA | Department of Communication

This research course explores the application of data science to real-world problems across sociology, psychology, public health, education, journalism, and political science. By the end of the research course, students will be able to critically analyze social data, apply machine learning techniques to real-world problems, and effectively communicate findings to diverse audiences. They will develop proficiency in using computational tools for data-driven decision-making and gain experience in addressing ethical considerations in data science applications.

Harvard | Center for International Development

As the field of data science continues to grow at an unprecedented pace, its applications are transforming industries, influencing policy decisions, and shaping the way organizations operate. This research course is designed to provide students with a foundational understanding of data science and its applications.
By the end of the research course, students will have gained the skills and confidence to independently perform data analysis, distinguish between correlation and causation, and critically assess the validity of data-driven conclusions.

Cambridge | Department of Applied Mathematics & Theoretical Physics

Climate change is among the most significant existential threats facing humanity, driving increased frequency and intensity of environmental hazards, ecosystem degradation, and widespread societal challenges.This research course aims to equip participants with the
knowledge and skills to analyze climate change and environmental risks using machine learning, bridging
scientific understanding and data-driven solutions.Students will have the opportunity to engage with algorithms and datasets, applying theoretical knowledge to pressing climate and environmental issues.

Cambridge | Department of Computer Science and Technology

This research course is an in-depth exploration of the realm of social networks, arguably the most important platform for collaboration and communication among the global population. We will explore the widespread adoption of social media platforms such as Facebook, Twitter, Instagram etc., which enable users to share diverse content like opinions, experiences, perspectives, and various media formats. Additionally, students will be taught cutting-edge methodologies for analysing and visualising data pertaining to social network structures and dynamics.

Oxford | Department of Engineering Science 

In this research course, students will gain an understanding
of how AI-based technologies are revolutionising healthcare.
Students will be introduced to biomedical sensors and
wearable systems and gain knowledge on the underlying
physiological phenomena. They will also learn to programme
in Python/MATLAB and implement their own AI pipeline on
healthcare data, from scratch.

Cambridge | Innovation Centre for Digital Molecular Technologies

In this multidisciplinary research course, students will explore the rise of machine learning (or artificial intelligence) with specific reference to drug discovery and chemical development, as well as emerging techniques for effective chemical/pharmaceutical synthesis. Combining topics from chemistry, biology, chemical engineering, and computer science, the research course equips students with the interdisciplinary knowledge needed to tackle cutting-edge problems in these fields.

National Institutes of Health (NIH) | National Cancer Institute

This research course explores how Artificial Intelligence (AI) is reshaping medical imaging, from improving diagnostic accuracy to enhancing patient outcomes. Students gain theoretical knowledge and practical experience in applying AI to various medical imaging modalities, learning about machine learning and deep learning techniques. Specific AI applications like computer-aided diagnosis systems and image reconstruction are examined, highlighting their potential to streamline healthcare workflows and benefit patients. Through independent research projects, students gain a deeper understanding of AI’s transformative impact on medical imaging and healthcare delivery.

Cambridge | Department of Engineering

Virtual Reality (VR) and Mixed Reality (MR) hold immense potential and represent the future of technology. They find applications in training simulations, gaming, healthcare therapies, architectural visualisation, manufacturing, and design. In this research course, students will be introduced to these cutting-edge technologies and learn how to unlock their potential through their research projects. During the research project, students will have the opportunity to choose from a variety of related topics, ranging from VR simulations to the development of tactile interfaces and psychophysical studies.

Oxford | Oxford Robotics Institute

This research course offers students a comprehensive understanding of Artificial Intelligence (AI) and Machine Learning (ML) in the context of robotics. It delves into advanced concepts and cutting-edge applications, catering to those interested in the intersection of AI and robotics. Students gain a strong foundation in AI/ML before exploring the intricacies of robotics, including its challenges and transformative potential across industries. Equipped with this knowledge, they are prepared to pursue careers in robotics, automation engineering, or AI research, ready to contribute meaningfully to this dynamic field.

Harvard | The Institute for Quantitative Social Science (IQSS)

This research course equips students with advanced statistical, machine learning, and AI methods to address complex social science issues such as political polarisation, gerrymandering, and criminal justice. It aims to demystify these methods and provide practical guidance on their evaluation and application. Through hands-on experience with real-world datasets, students learn to use tools like GitHub and cloud computing for analysis. The course covers a range of methods including Ordinary Least Squares, Bayesian statistics, Large Language Models, and survey methods, preparing students to communicate results effectively to policymakers and the public.

Cambridge | Department of Applied Mathematics and Theoretical Physics

This research course will provide an introduction to quantum processes. We will begin by expounding the principles of quantum mechanics in our setting (and Dirac notation) and then immediately make connections to information (quantum states viewed as information carriers, quantum teleportation) and computation (notion of qubits and quantum gates). At the same time, we will discuss quantum cryptography (quantum key distribution), and quantum computing, culminating in an exposition of principal quantum algorithms, such as the Deutsch-Jozsa algorithm. While no previous knowledge of quantum physics is required for this course, a relatively strong background in mathematics or physics would be beneficial.

Princeton | Department of Chemistry

This research course focuses on classic light-matter interactions, delving into the realm of physical chemistry, with a special emphasis on photoluminescence. Throughout the course, students will grasp the fundamental principles of light-matter interactions, including basic quantum mechanics to extend their understanding from classical to quantum physics. Through hands-on experimentation, they will cultivate a deeper understanding of the dynamic processes that govern the interplay of light and matter.

Cambridge | Centre for Quantum Information and Foundations

Quantum physics is confirmed with overwhelming experimental evidence at the microscopic scales (e.g., at the atomic scale), producing many technological applications. This research course will address the foundational issues of quantum physics as it relates to quantum measurement and general relativity. Students with a relatively strong background in mathematics or physics would excel in this research course.

Cambridge | Centre for Quantum Information and Foundations

General relativity (GR) is one of our two fundamental theories of physics (together with quantum theory). According to general relativity, space and time are part of a single entity called spacetime, whose geometry depends on the distribution of energy and mass according to Einstein’s equations. In this research course we will discuss the theory of general relativity and some fascinating applications and important open problems, including curved spacetime, gravitational lensing, black holes, gravitational waves, and more.

MIT | Kavli Institute for Astrophysics and Space Research 

Astronomy is entering an unprecedented era of big data, as
new facilities are observing more phenomena than humans
can possibly visually examine. Dealing with millions of
astronomical objects and producing terabytes of data every
day requires machine learning and statistical methods to
classify, model, and characterise the data influx. In this
research course, we will learn cutting-edge machine-learning
methods and apply them to real astronomical datasets to
discover, model, and further our understanding of the
universe

MIT | Kavli Institute for Astrophysics and Space Research 

The measurement of cosmic distances provides the essential framework for understanding the origin, evolution, and fate of our universe. This research course explores the intricate “cosmic distance ladder” – the interconnected methods astronomers use to measure distances from our solar neighborhood to the edge of the observable universe. Through hands-on projects with real astronomical datasets, students will answer one of the most fundamental questions, what is the origin, structure, and future of the universe?

Oxford | Department of Physics 

White dwarfs, neutron stars and black holes are compact
objects forming at the final stages of the evolution of
massive stars. In this research course, we will learn the
nature of compact objects and see their place in the history
of the universe. During the research course, we will touch
on many topics from high energy astrophysics and talk
about the recent progress in the detection of gravitational
waves. Finally, we will discuss open issues standing in front
of the scientific community and try to figure out how further
steps in the investigation of black holes, neutron stars, and
white dwarfs will help in probes of fundamental physics
under extreme conditions.

Oxford | Department of Physics

This research course deals with the structure and evolution of isolated stars and starts in binary systems.Through a blend of theoretical concepts, observational data, and computational models, student will gain an understanding of the physical phenomena governing stars’ evolution. Throughout the research course, students will engage in hands-on activities, computer simulations, and observational projects to reinforce theoretical concepts and gain practical skills in data analysis.

Oxford | Beecroft Institute for Particle Astrophysics and Cosmology

Our standard model of cosmology posits that around 85% of the matter in the universe is “dark matter”: an elusive, invisible, hypothetical substance that interacts noticeably with ordinary matter only through gravity. A key challenge in astrophysics is mapping out dark matter using subtle observations that give us clues to its gravitational influence, such as the arrangement of billions of galaxies photographed by telescopes and the bending of light by dark matter’s gravity. We will gain hands-on experience with advanced statistical techniques and machine learning methods, utilizing the same tools used by leading academic researchers in the field that allows us to unravel its secrets.

University of Toronto | Canadian Institute for Theoretical Astrophysics

Black holes are among the most enigmatic and extreme objects in our universe. Though they consist solely of gravity, the no-hair theorem tells us that their only defining properties are mass, spin, and charge. Despite this simplicity, they give rise to some of the most complex and energetic phenomena in astrophysics. In this research course, we will explore what black holes are and how we can “see” them—not directly, but through the radiation emitted by matter and plasma in their immediate vicinity.