University Assistant/Associate Professor

University of Cambridge
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

Postdoctoral Research Associate in Statistical Genetics and Machine Learning (Fixed Term)

Postdoctoral Research Associate in Statistical Genetics and Machine Learning (Fixed Term)

Math Assistant Professor — Data Science & Global Teaching

Assistant Professor in Actuarial Data Science (T&R)

Assistant Professor in Statistical Data Science

Assistant Professor of Mathematics — Data Science & Global Teaching

We welcome excellent candidates who can teach and perform research in Plant Ecology and will complement our expertise in forest ecology, conservation, and evolution. In particular, we are interested in applications from those with a focus on fieldwork, collection-based approaches, or big data analysis in areas such as community ecology, plant and microbial responses to climate change, forestry, and the global restoration and conservation agendas.

The appointee will join the Department of Plant Sciences, which delivers teaching and research that stretches across the plant sciences discipline - from ecology and conservation, to physiology, genetics and genomics. There will be the opportunity to be affiliated with the Cambridge Conservation Research Institute, a world-leading centre for research and training in biodiversity conservation, working in partnership with ten international conservation organisations.

Key responsibilities include teaching undergraduates, conducting world-leading research, and advising postgraduate students and post-doctoral workers. There is the opportunity to become a Fellow of a College. Undergraduate teaching includes lectures, practical classes, and small-group supervisions. The teaching commitment also includes an annual field course (either in England, Portugal or Borneo). Postgraduate training options include contribution to MPhil and PhD programmes. Innovations in teaching methods are actively encouraged, as are engagement with mentoring and professional development.

We will support the successful candidate to develop a research programme that complements and reinforces existing Departmental research strengths. There are shared laboratory facilities for a variety of environmental analyses both within the Department and in collaboration with other Departments.

Candidates must have a PhD in a relevant field and sufficient breadth and/or depth of specialist knowledge in their chosen discipline to be able to teach to a high standard. They should also have evidence of excellence in research and demonstrated capacity to develop research objectives, projects and proposals. Appointments at the level of Associate Professor will usually be able to provide evidence of success in obtaining research funding to support research or career development.

Applications should comprise a letter of application (summarise how you would contribute to both teaching and research in the Department), your curriculum vitae (including up-to-date publications list, and for three outputs a concise narrative outlining the context for each piece of work, your contribution to the output, and the importance of the findings), and a statement of your current and future research plans (no more than five pages in length).

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

Appointment will be either at Grade 9 (£45,585-£57,696) or Grade 10 (£61,198- £64,914), depending on skills and experience.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.