Department of Chemistry course timetable

This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course.

In this short talk we will cover what supervisions are, the role they play in Cambridge teaching, and how supervisors are recruited. We will then go on to look at how you can prepare for supervising, how you can conduct a supervision, and how to deal with common pitfalls.

This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course.

This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course.

FS1 - Successful Completion of a Research Degree An hour devoted to a discussion of how to plan your time effectively on a day to day basis, how to produce a dissertation/thesis (from first year report to MPhil to PhD) and the essential requirements of an experimental section.

FS2 - Dignity@Study The University of Cambridge is committed to protecting the dignity of staff, students, visitors to the University, and all members of the University community in their work and their interactions with others. The University expects all members of the University community to treat each other with respect, courtesy and consideration at all times. All members of the University community have the right to expect professional behaviour from others, and a corresponding responsibility to behave professionally towards others. Nick will explore what this means for graduate students in this Department and the session will conclude with tea/coffee and biscuits, in order to provide an opportunity to ask questions more informally.

This is a compulsory session for 1st year post-graduates.

This is a compulsory session which introduces new graduate students to the Department of Chemistry Library and its place within the wider Cambridge University Library system. It provides general information on what is available, where it is, and how to get it. Print and online resources are included.

You must choose one session out of the 9 sessions available.

The CCDC is the home of small molecule crystallography data and is a leader in software for pharmaceutical discovery, materials development, research and education.

The CCDC compiles and distributes the Cambridge Structural Database (CSD), the world's repository of experimentally determined organic and metal-organic crystal structures. It also produces associated knowledge-based application software for the global community of structural chemists, delivered through the CSD-System, CSD-Discovery, CSD-Materials and CSD-Enterprise.

The CCDC originated in the Department of Chemistry at the University of Cambridge, and is now a fully independent institution constituted as a non-profit company and a registered charity.

Ian Bruno, Head of Strategic Partnerships at the CCDC, will present this session. Ian will firstly introduce the CCDC and the CSD, and will then focus on enablers for FAIR (Findable, Accessible, Interoperable, Reusable) chemistry data, including standard identifiers (e.g. InChI), standard file formats (e.g. for spectra), open file formats (e.g. for structures), and vocabularies.

A second session will follow on identifiers and extensions to the InChI that have been introduced in this session, to be presented by Professor Jonathan Goodman (IS9 course).

Refreshments (tea, coffee, biscuits) are included for this session.

This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course.

This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course.

This compulsory session introduces Research Data Management (RDM) to Chemistry PhD students. It is highly interactive and utilises practical activities throughout.

Key topics covered are:

• Research Data Management (RDM) - what it is and what problems can occur with managing and sharing your data.
• Data backup and file sharing - possible consequences of not backing up your data, strategies for backing up your data and sharing your data safely.
• Data organisation - how to organise your files and folders, what is best practice.
• Data sharing - obstacles to sharing your data, benefits and importance of sharing your data, the funder policy landscape, resources available in the University to help you share your data.
• Data management planning - creating a roadmap for how not to get lost in your data!

The InChI is a standard identifier for molecules, which has initiated the discussion of many other possibilities for molecular identifiers. This talk will explain how the development of the InChI has led to the development of the InChIKey, and to extensions of the system including reactions (RInChI), mixtures (MInChI), tautomers, variable structures, organometallics, biologics and QR codes.

Artificial Intelligence (AI) in the context of chemistry has a long history. The first application was in mass spectrometry, but AI is now being applied to a diverse range of problems, including reaction prediction and drug discovery. Machine learning (ML) is an important part of AI, and the aim of this course is to introduce some of the main ML concepts and techniques, and to illustrate their use in contemporary chemical applications. By the end of the course, you should be able to judge which of these ML techniques are appropriate for a given task and evaluate the results.

Artificial Intelligence (AI) in the context of chemistry has a long history. The first application was in mass spectrometry, but AI is now being applied to a diverse range of problems, including reaction prediction and drug discovery. Machine learning (ML) is an important part of AI, and the aim of this course is to introduce some of the main ML concepts and techniques, and to illustrate their use in contemporary chemical applications. By the end of the course, you should be able to judge which of these ML techniques are appropriate for a given task and evaluate the results.

A ‘recommended’ optional course introducing electronic databases SciFinder and Reaxys presented by Professor Jonathan Goodman comprising of presentation followed by hands-on investigation.

Please bring your own laptop for the practical element of the session.

Personal registration required for access to SciFinder. Please see the prerequisites.

Artificial Intelligence (AI) in the context of chemistry has a long history. The first application was in mass spectrometry, but AI is now being applied to a diverse range of problems, including reaction prediction and drug discovery. Machine learning (ML) is an important part of AI, and the aim of this course is to introduce some of the main ML concepts and techniques, and to illustrate their use in contemporary chemical applications. By the end of the course, you should be able to judge which of these ML techniques are appropriate for a given task and evaluate the results.

A ‘recommended’ optional course for Chemistry graduates that introduces all the relevant online databases available to you in the university: citation databases such as Web of Science, Scopus, and PubMed, which index all the scientific literature that is published, as well as chemistry and related subject-specific databases. You will be guided on how to search citation databases effectively and the session includes a hands-on element where you can practice - please bring your own laptop.

The session will be most suitable for those who are new to searching citation databases or would like a refresher.

Please note that this session will not cover searching the databases Reaxys and SciFinder. These are covered by IS5.

Please bring your own laptop so you can participate in the practical element of the session.

Artificial Intelligence (AI) in the context of chemistry has a long history. The first application was in mass spectrometry, but AI is now being applied to a diverse range of problems, including reaction prediction and drug discovery. Machine learning (ML) is an important part of AI, and the aim of this course is to introduce some of the main ML concepts and techniques, and to illustrate their use in contemporary chemical applications. By the end of the course, you should be able to judge which of these ML techniques are appropriate for a given task and evaluate the results.

Much of scientific knowledge and information is communicated in written form, be it via journal publications, theses or in other media. However, scientific writing differs from other styles of writing quite significantly, with regard to structure, grammar and word choice. This lecture will outline the basics of what to consider when 'writing science', in order to smoothen the path to your first peer-reviewed publication, as well as your later thesis.

This compulsory course will equip you with the skills required to manage the research information you will need to gather throughout your graduate course, as well as the publications you will produce yourself. It will also help you enhance your online research profile and measure the impact of research.

Artificial Intelligence (AI) in the context of chemistry has a long history. The first application was in mass spectrometry, but AI is now being applied to a diverse range of problems, including reaction prediction and drug discovery. Machine learning (ML) is an important part of AI, and the aim of this course is to introduce some of the main ML concepts and techniques, and to illustrate their use in contemporary chemical applications. By the end of the course, you should be able to judge which of these ML techniques are appropriate for a given task and evaluate the results.