skip to navigation skip to content

Department of Chemistry

Department of Chemistry course timetable

Show:

Thu 21 Nov – Fri 24 Jan 2020

Now Today



November 2019

Thu 21
Chemistry: IS10 Reproducible Research and Software: With a Python Focus new [Places] 14:30 - 15:00 Unilever Lecture Theatre

Research reproducibility can be hard to get right. The aim of this talk is to raise awareness on the common pitfalls so you can confidently share your work for posterity. We will cover the dos and don’ts of data processing, how to comment on a script, and how to share it. Python will be used as an example because a variety of tools exist for this language. The goal is for anyone reading your paper to be able to go from the raw data to your paper figures.

The talk will last 20 minutes and there will be time for questions/discussion afterwards.

This talk is brought to you by the Chemistry Data Champions https://www-library.ch.cam.ac.uk/chemistry-data-champions

Fri 22
Chemistry: CDT Introduction to Probablistic Modelling new Not bookable 13:00 - 17:00 Todd-Hamied

An applied introduction to probabilistic modelling, machine learning and artificial intelligence-based approaches for students with little or no background in theory and modelling. The course will be taught through a series of case studies from the current literature in which modelling approaches have been applied to large datasets from chemistry and biochemistry. Data and code will be made available to students and discussed in class. Students will become familiar with python based tools that implement the models though practical sessions and group based assignments.

Mon 25
Chemistry: CT10 Vibrational Spectroscopy new [Places] 10:00 - 12:00 Unilever Lecture Theatre

Spectroscopic methods in biochemistry and biophysics are powerful tools to characterise the chemical properties of samples in chemistry and biology, including molecules, macromolecules, living organisms, polymers and materials. Within the wide class of biophysical methods, infrared spectroscopy (IR) is a sensitive analytical label-free tool able to identify the chemical composition and properties of a sample through its molecular vibrations, which produce a characteristic fingerprint spectrum. An infrared spectrum is commonly obtained by passing infrared radiation through a sample and determining what fraction of the incident radiation is absorbed at a particular energy. The energy at which any peak in an absorption spectrum appears corresponds to the frequency of a vibration of a part of a sample molecule. One of the great advantages of infrared spectroscopy is that virtually any sample in virtually any state may be studied, such as liquids, solutions, pastes, powders, films, fibres, gases and surfaces can all be examined. In this introductory course, the basic ideas and definitions associated with infrared spectroscopy will be described. First, the possible configurations of the spectrometers used to measure IR absorption will be discussed. Then, the vibrations of molecules, inorganic and organic chemical compounds, as well as large biomolecules will be introduced, as these are crucial to the interpretation of infrared spectra in every day experimental life.

Wed 27
Chemistry: IS3 Research Information Skills [Places] 10:00 - 12:00 Unilever Lecture Theatre

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.

Thu 28
Chemistry: IS4 Research Data Management [Standby] 11:00 - 13:00 Todd-Hamied

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!

Lunch and refreshments are included for this course

December 2019

Mon 2

The main aim of giving a presentation to the public or a science venue is to present information in a way that the audience will remember at a later time. There are several ways in which we can improve this type of impact with an audience. This interactive lecture explores some of those mechanisms.

Tue 3
Chemistry: Green Chemistry new (1 of 2) [Places] 09:00 - 13:00 Unilever Lecture Theatre

This course will provide an overview of Sustainable Chemistry in the Pharmaceutical Industry: Motivation and Legislation It will cover the following in more detail;

  • Solvents - tools for analysing the merits and drawbacks of different solvents and tools for selecting the optimum solvent for chromatography, common reactions, work-ups and other purposes
  • Reagents - tools for analysing the merits and drwabacks of different reagents and substrate scope for some greener reagents for common transformations
  • Metrics: Yield, Atom Economy, Reaction Mass Efficiency, E-factor, Process Mass Intensity, Life Cycle Analysis and Carbon Footprinting
Chemistry: Green Chemistry new (2 of 2) [Places] 13:00 - 17:00 Unilever Lecture Theatre

This course will provide an overview of Sustainable Chemistry in the Pharmaceutical Industry: Motivation and Legislation It will cover the following in more detail;

  • Solvents - tools for analysing the merits and drawbacks of different solvents and tools for selecting the optimum solvent for chromatography, common reactions, work-ups and other purposes
  • Reagents - tools for analysing the merits and drwabacks of different reagents and substrate scope for some greener reagents for common transformations
  • Metrics: Yield, Atom Economy, Reaction Mass Efficiency, E-factor, Process Mass Intensity, Life Cycle Analysis and Carbon Footprinting
Wed 4

The main aim of giving a presentation to the public or a science venue is to present information in a way that the audience will remember at a later time. There are several ways in which we can improve this type of impact with an audience. This interactive lecture explores some of those mechanisms.

This session will require 4-5 volunteers to provide a 10 min talk which the session will show how to improve. Presenters in the following week's Peer to Peer presentations will be given priority booking for this event.

Thu 5
Chemistry: CT8 Electron Microscopy CANCELLED 14:00 - 15:30 Unilever Lecture Theatre

This lecture will provide an overview of the Department’s electron microscopy facility. It will cover the theory of Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), including cryo-TEM and tomography, as well as analytical techniques Energy-dispersive X-ray spectroscopy (EDX) and Electron Energy Loss Spectroscopy (EELS). Examples of how these techniques can be used to characterise a range of samples including polymers, proteins and inorganic materials will be shown.

Fri 6
Chemistry: FS3 Integrity and Ethics in Research [Places] 14:00 - 16:00 Unilever Lecture Theatre

A thorough awareness of issues relating to research ethics and research integrity are essential to producing excellent research. This session will provide an introduction to the ethical responsibilities of researchers at the University, publication ethics and research integrity. It will be interactive, using case studies to better understand key ethical issues and challenges in all areas. There are three sessions running, you need attend only one.

January 2020

Mon 13
Chemistry: SC1-10 Statistics for Chemists (1 of 8) [Standby] 13:00 - 15:00 G30

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.

Tue 14
Chemistry: IS4 Research Data Management [Places] 10:00 - 12:00 Todd-Hamied

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!

Lunch and refreshments are included for this course

Chemistry: Machine Learning in Chemistry 101 new (1 of 7) [Full] 13:00 - 14:00 Chemistry of Health

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

Wed 15
Chemistry: SC1-10 Statistics for Chemists (2 of 8) [Standby] 10:00 - 12:00 G30

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.

Chemistry: DD1 The Drug Discovery Process [Places] 14:00 - 15:00 Unilever Lecture Theatre

Drug discovery is a complex multidisciplinary process with chemistry as the core discipline. A small molecule New Chemical Entity (NCE) (80% of drugs marketed) has had its genesis in the mind of a chemist. A successful drug is not only biologically active (the easy bit), but is also therapeutically effective in the clinic – it has the correct pharmacokinetics, lack of toxicity, is stable and can be synthesised in bulk, selective and can be patented. Increasingly, it must act at a genetically defined sub-population of patients. Medicinal chemists therefore work at the centre of a web of disciplines – biology, pharmacology, molecular biology, toxicology, materials science, intellectual property and medicine. This fascinating interplay of disciplines is the intellectual space within which a chemist has to make the key compound that will become an effective medicine. It happens rarely, despite enormous investment in time, money and effort. What factors make a program successful? I would like to briefly outline the process, but importantly to offer some key with examples of success

Thu 16
Chemistry: Machine Learning in Chemistry 101 new (2 of 7) [Full] 13:00 - 14:00 Chemistry of Health

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

Fri 17
Chemistry: DD2 The Drug Discovery Process [Places] 14:00 - 15:00 Unilever Lecture Theatre

Drug discovery is a complex multidisciplinary process with chemistry as the core discipline. A small molecule New Chemical Entity (NCE) (80% of drugs marketed) has had its genesis in the mind of a chemist. A successful drug is not only biologically active (the easy bit), but is also therapeutically effective in the clinic – it has the correct pharmacokinetics, lack of toxicity, is stable and can be synthesised in bulk, selective and can be patented. Increasingly, it must act at a genetically defined sub-population of patients. Medicinal chemists therefore work at the centre of a web of disciplines – biology, pharmacology, molecular biology, toxicology, materials science, intellectual property and medicine. This fascinating interplay of disciplines is the intellectual space within which a chemist has to make the key compound that will become an effective medicine. It happens rarely, despite enormous investment in time, money and effort. What factors make a program successful? I would like to briefly outline the process, but importantly to offer some key with examples of success

Mon 20
Chemistry: SC1-10 Statistics for Chemists (3 of 8) [Standby] 10:00 - 12:00 G30

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.

Tue 21
Chemistry: Machine Learning in Chemistry 101 new (3 of 7) [Full] 13:00 - 14:00 Chemistry of Health

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

Wed 22
Chemistry: SC1-10 Statistics for Chemists (4 of 8) [Standby] 10:00 - 12:00 G30

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.

Chemistry: Machine Learning in Chemistry 101 new (4 of 7) [Full] 13:00 - 14:00 Unilever Lecture Theatre

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

Thu 23

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 with an opportunity to ask questions more informally.

This is a compulsory session for 1st year postgraduates.

Chemistry: Machine Learning in Chemistry 101 new (5 of 7) [Full] 13:00 - 17:00 Chemistry of Health

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

Fri 24
Chemistry: DD3 Modern Tactics to Optimise Potency [Places] 14:00 - 15:00 Unilever Lecture Theatre

When you have 1000s of possible compounds you could make from any one start point what do you make first? This lecture will cover some general basic principles on designing more potent molecules, as well as some practical tips on how to run an optimization program and how to focus synthetic efforts. Binding modalities (reversible, covalent) will be briefly covered, as well as some newer non-traditional modalities. This lecture will also serve as an introduction to the medicinal chemistry game.