This exclusive programme will give participants the opportunity to attend lectures focusing on Science, Technology, Engineering and Mathematics. Through attending lectures and tutorial, delegates will gain an understanding of the various fields within the science spectrum. Oxford is famed for its advanced research facilities and Nobel Prize winners for scientific discoveries. Changes to course descriptions, topics, programme structure, and schedules may occur depending on the availability of faculty members at the time of the programme.

Materials Engineering: Dynamic Behaviour of Materials

This course will introduce you to the effect of rapidly applied loads to different categories of solid materials. It will focus on direct industrial applications, providing the required tools to fully understand the dynamic behaviour of materials, and how they are used in the design process of real-life components used in extreme conditions, from aerospace to defence applications. The first part of the course will introduce the concept of dynamic behaviour and wave propagation in solid materials. The second part of the course will expand the range of deformation mechanisms by including plastic and shock waves. The Final part of the course will focus on techniques use to characterise solid materials at different loading rates and on the design of materials able to withstand extreme conditions, such as impacts and explosions.

Biochemistry and Molecular Biology: Protein structure and function to Therapy and Disease

This course is an integrated introduction to the structure of macromolecules and a biochemical approach to protein function. We will take you through the organisation of macromolecules and study of their assembly into complexes responsible for specific biological processes. Topics addressing protein folding and function including enzyme kinetics, protein post-translational modifications the characterization of major metabolic pathways and their interconnection into tightly regulated networks. Current laboratory techniques and experimental design are discussed during the weeks and examples showing the organization of protein networks and how they relate to human diseases and therapy. There will also be an exposure to current research papers with an emphasis on viral pathogenesis to illustrate the themes discussed including vaccines and drug design. This will allow participants to build their critical analysis and apply scientific rationale to some of the latest findings in the field.

Artificial Intelligence: Logic, Knowledges Representations and Ontologies

Knowledge Representation is at the heart of the great challenge of Artificial Intelligence: to understand the nature of intelligence and cognition so that computers can exhibit human-like abilities. Pioneers of the field such as John McCarthy believed that (artificial) intelligence could be formalised as symbolic reasoning with explicit representations of human knowledge given in some form of Logic. In this setting, the key challenge is to effectively represent knowledge in computers and to exploit it algorithmically to perform tasks in an intelligent way. Since its very inception, Knowledge Representation has been an inter-disciplinary field which lies at the intersection between Logic and Metamathematics, Philosophy, and Computer Science. This course focuses on general methods for representing human knowledge in Artificial Intelligence and will be given mostly from a Computer Science perspective. The course will cover not only foundations and algorithms, but also Semantic and Ontology-based Technologies and their role on modern Information Systems.

Quantum computing, collapse, and consciousness

This course will look at two fundamental aspects of nature and their possible connection: quantum theory and consciousness. We will cover the basics of quantum computing, the measurement problem in quantum mechanics, and the mathematical modelling of consciousness.

The first week of the course will focus on quantum computing. We will introduce all the basic definitions from quantum theory and computation, and finish by describing Shor’s quantum algorithm for the factorisation of integers.

In the second week we will look more closely at the notion of measurement in quantum theory. We will discuss the measurement problem and possible solutions, focusing on collapse models. We will then discuss the mathematical modelling of consciousness and use collapse models as a possible scientific theory of consciousness. The course will only assume good knowledge of linear algebra. Some knowledge of differential equations is recommended but not essential.

Decoding Virology: Insights from the virus-host co-evolutionary arms race

This course will delve into the fundamentals of virology, immunology and cell biology to provide insights on how viruses cause disease, and what we can do about it. Participants will gain an understanding on how viruses and hosts interact and evolve over time, the principles of emergence of new viral variants, their impact on host immune responses and how medical interventions can be developed to target them. Through lectures and case studies, students will examine current research and development in the field of virology, and its implication in vaccine and drug development. This course therefore aims to provide a comprehensive overview of this field, offering students a unique perspective on the emergence of viruses and their ability to cause outbreaks.

Artificial Intelligence: Symbolic Methods in AI

Symbolic methods in AI assume that knowledge can be represented by means of symbols that can further be manipulated in order to perform reasoning and problem-solving tasks. These methods rely on formal logic and symbolic representations, such as knowledge graphs or rule-based systems, to model relationships and infer new information. Since symbolic AI can be successfully exploited in applications requiring explicit rules and structured knowledge, such as expert systems and natural language processing, during the course we will discuss the most important tools used in this paradigm of AI, such as propositional and first-order logic, description logics, knowledge graphs, or the Datalog language.

For details please see our Academics Team page: https://oxfordsummerabroad.com/academics-team/