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United Kingdom, Glasgow

Closing date: 03/01/2025
Salary Range: £34,866
FTE: 1
Contract Type: Fixed Term (1 year)

The Research Assistant, alongside PhD and other PDRA workers assigned to the project will design and enable the synthesis of novel small molecule inhibitors of the Androgen Receptor. This will require a thorough grounding in synthetic organic chemistry, allied with experience of array synthesis approaches, as well as associated purification and analysis techniques. From consideration of biological data generated by programme team colleagues, the PDRA (with input from the PI) will lead on the design and synthesis of new compounds which meet agreed lead candidate criteria. Direct experience of academic or industrial medicinal chemistry is an advantage but not considered essential. The PDRA is expected also to engage in knowledge exchange processes with Aberdeen colleagues and other external partners as appropriate, as well as assisting in the administrative duties associated with the group of Prof Jamieson at the University of Strathclyde.

For informal enquires, please contact Professor of Medicinal Chemistry, Craig Jamieson (craig.jamieson@strath.ac.uk).

please click here for further details


University of Strathclyde

Finland

Samuel Kaski’s research group Probabilistic Machine Learning is searching for postdocs to work on AI fundamentals in exciting projects. The work includes collaboration with the Finnish Center for Artificial Intelligence (FCAI), the Centre for AI Fundamentals at the University of Manchester, the Alan Turing Institute, ELLIS and the new ELLIS Institute Finland, and researchers from other fields.

Prof Kaski is Professor of Computer Science in Aalto University and Professor of AI in the University of Manchester. He is Director of Finnish Center for Artificial Intelligence and ELLIS Unit Helsinki. His research group develops machine learning principles and methods focusing on a few key topics (see “Machine learning foundations” below), often working with researchers of other fields in new exciting applications (see the other topics below).

Topics

Machine learning foundations

Keywords: probabilistic modelling, Bayesian inference, simulation-based / likelihood-free inference, multi-agent RL and collaborative AI, sequential decision making and experimental design, privacy-preserving learning, Bayesian deep learning, generative models

We are looking for a new postdoc in the team which develops probabilistic modelling and Bayesian inference methods. The team has several exciting new machine learning formulations we work on, and opportunities for applying the methods with top-notch collaborators. The core is always development of new methods, and with this call we are looking for talented researchers with background in machine learning, stats or CS (or other directly relevant topics) who are keen on developing the new methods. In the cover letter, let us know what you are interested in - if we are already working on it, all the better, but we are willing to listen to new ideas too.

Machine learning for synthetic biology and biodesign

Keywords: AI-based design, human-in-the-loop machine learning, collaborative AI, molecular modeling, graph neural networks, deep learning algorithms, generative models

We are searching for early career scientists to join our research team working towards next-generation machine learning methods for synthetic biology. The positions are available in two large multi-year projects (BIODESIGN and BIOFOUNDRY) focused on establishing virtual laboratories for protein engineering and biomanufacturing, implemented in collaboration between FCAI and VTT Technical Research Centre of Finland, and NSF Global Center on biofoundries. The aim of the projects is to design novel AI-based tools for bioengineering. These tools will combine cutting edge machine learning techniques, such as graph neural networks, deep generative models, Bayesian optimization, and human-in-the-loop learning with the synthetic biology expertise of VTT. The virtual laboratories are envisioned to have a wide range of applications in industry (e.g., new biochemicals, biomaterials and drugs), and to help the transition to a carbon-neutral society.

Applicants should have a strong academic record in computer science, mathematics, or statistics. Solid research experience in one or more of the following fields is beneficial: AI-based design, Deep learning algorithms, Generative models, Human-in-the-loop machine learning, Collaborative AI, Molecular modeling, Reinforcement learning, Structured prediction.

The successful applicants will join a world-class research team with top AI researchers from FCAI and synthetic biology experts from VTT.

Real-time AI-assistance with computationally rational user models

Keywords: collaborative AI, human-in-the-loop ML/RL, reinforcement learning, user modelling

Despite recent advances in AI and machine learning, there are few AI solutions that can be applied when we cannot precisely specify the objective or do not have suitable data to learn from. This is particularly salient and challenging in ad hoc assistance for human users in complex tasks such as scientific discovery. 

This project aims to address this issue by learning these objectives, and to assist, directly from interaction with the users. To do so, we develop reinforcement learning techniques for AI-assistants and computationally rational user models that take into account factors such as cognitive biases to reason about human decision-making. We push state-of-the-art in AI that, through inference over - and planning with - such user models, is able to tackle problems that are otherwise out of reach of mainstream AI solutions. 

Supported by a High-performance computing grant from the Research Council of Finland, we are looking to strengthen this team with postdocs with a strong academic record in computer science, mathematics, or statistics. Solid research experience in one or more of the fields listed under this topic is beneficial.

Team: this project will be carried out with a top expert on computational rationality, Prof. Andrew Howes of University of Exeter,  and experts in application domains.

Next generation distribution shifts

Keywords: distribution shift, out-of-distribution generalization, robust deployment, deep learning, experimental design

Robust algorithms for machine learning models are vital as the deployed scenario can differ from the training scenario in unexpected ways. Placing assumptions on the kind of distribution shift we expect, such as covariate or label shift, and accounting for it during training is common practice. However, these assumptions are often too restrictive for real-world settings, as the shifts encountered are typically more complex. Moreover, it is difficult to know a priori which shifts we need to account for. This project focuses on developing novel machine learning methods  that demonstrate robust performance by adapting to unforeseen shifts and diverse datasets during deployment.

We are looking for applicants with a strong academic record in computer science, mathematics, or statistics. Solid research experience in one or more of the following fields is beneficial: distribution shift, out-of-distribution generalization, out-of-domain generalization, sequential experimental design.

Your experience and ambitions

We expect the candidates to hold or be close to getting a relevant doctoral degree and have a solid background in the mathematics/statistics/computer science needed in machine learning.

Previous experience in the application fields is an advantage. Capability of both independent work and teamwork, and excellent written and spoken English are necessary.

What we offer

We provide

RESEARCH ENVIRONMENT

You will work in Professor Samuel Kaski’s research group (Probabilistic Machine Learning Group). We design collaborations as we go, according to what the research needs. Collaborators include but are not restricted to the other groups in the Finnish Center for Artificial Intelligence (FCAI), other sites of the European Laboratory for Learning and Intelligent Systems (ELLIS) and our new ELLIS Institute Finland, Centre for AI Fundamentals of the University of Manchester, the Turing Institute, and a number of excellent researchers in other fields in our applications. 

JOB DETAILS

Postdoc positions are typically made for up to three years. Starting dates are flexible. All positions are negotiated on an individual basis. We are strongly committed to offering everyone an inclusive and non-discriminating working environment. We warmly welcome qualified candidates from all backgrounds to apply and particularly encourage applications from women and other groups underrepresented in the field.

All our positions are fully funded and the salary is based on the Finnish universities’ pay scale. The starting salary depends on the level of the position and the previous experience and is typically increased as the experience grows. All employees have access to the occupational health care services and are covered by the Finnish national health insurance system.

Ready to apply?

Submit your application through our recruitment system Workday by the link below. The deadline for applications is  February 2 2025 at 23:59 Finnish time, 2024.

Required attachments


	Cover letter (1–2 pages). 
	CV
	List of publications (please do not attach full copies of publications)
	A transcript of doctoral study
	The degree certificate of your latest degree. If you don’t yet have a PhD degree, a plan of completion must be submitted.
	Contact details of two senior academics who can provide references. We will contact your referees if we need recommendation letters.


All materials should be submitted in English in a PDF format. Note: You can upload max. five files to the recruitment system, each max. 5MB.

Please note: Aalto University’s employees should apply for the position via our internal system Workday -> find jobs (not external aalto.fi webpage on open positions) by using their existing Workday user account.

Contacts: Coordinator Fang Wang (fang.wang@aalto.fi)

More Information

We are part of Finnish Centre for Artificial Intelligence FCAI and ELLIS Unit Helsinki. More information on their pages, and the frequently asked questions on this page.  

Aalto University (53rd in the Times Higher Education THE ranking list of most international universities in the world) is a community of bold thinkers where science and art meet technology and business. We are committed to identifying and solving grand societal challenges and building an innovative future. Aalto has six schools with nearly 12 000 students and a staff of more than 4000, of which 400 are professors. Our main campus is located in Espoo, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community.

The Department of Computer Science is an internationally-oriented community and home to world-class research in modern computer science, combining research on foundations and innovative applications. With over 40 professors and more than 450 employees from 50 countries, it is the largest department at Aalto University and the largest computer science unit in Finland. Computer science research at Aalto University ranks high in several prominent surveys (47th worldwide and 9th in Europe in Shanghai subject ranking 2019; and 56th worldwide in Times Higher Education subject ranking 2020).

About Finland

Finland is a great place for living with or without family – it is a safe, politically stable and well-organized Nordic society. Finland is consistently ranked high in quality of life and was just listed again as the happiest country in the world: https://worldhappiness.report/news/its-a-three-peat-finland-keeps-top-spot-as-happiest-country-in-world/. For more information about living in Finland: https://www.aalto.fi/services/about-finland

More info:

twitter.com/fcai_fi
linkedin.com/company/fcai
youtube.com/channel/UC7nUhposDgxzDOKns_H5J0w
Newsletter: http://eepurl.com/gVPBpf

Aalto.fi 

twitter.com/aaltouniversity 
facebook.com/aaltouniversity
instagram.com/aaltouniversity

More about Aalto University:

Aalto.fi
youtube.com/user/aaltouniversity
linkedin.com/school/aalto-university/
www.facebook.com/aaltouniversity
instagram.com/aaltouniversity
twitter.com/aaltouniversity


Aalto University

Australia, Geelong


	Based at our Geelong, Waurn Ponds Campus
	Part-time 0.6FTE and fixed-term for 2 years
	Level A $76,985 - $103,493 + 17% Superannuation (pro rata, part time)


Who are we?

Deakin University stands as a shining beacon of excellence in the world of higher education.  Deakin is renowned for its unwavering commitment to innovation, research, and inclusivity. With a strong emphasis on cutting-edge research and a diverse, globally-connected community, Deakin provides an unparalleled educational experience. Here, students not only gain knowledge but also develop the skills and mindset needed to thrive in a rapidly evolving world. Discover the thrill of intellectual exploration and let Deakin's passion for learning and innovation ignite your own, as you prepare to shape the future with confidence and purpose.

The Faculty of Health is dedicated to being at the forefront of teaching, learning, creating and social development and fostering cutting-edge research and discovery. The School of Medicine places high emphasis on research that makes a difference and is solution-led. We work closely with industry partners and the government to put findings into practice.

The associate lecturer will contribute academic to the delivery of the anatomy and pathology practical program in the School of Medicine. They will work with other team members to facilitate student learning of anatomy and pathology across the schools’ programs. The appointee will promote the Faculty within the academic and professional communities in Australia, contributing to the Faculty’s teaching and program development initiatives. The appointee will liaise with students at all levels.

As Associate Lecturer in Medical Science, you will:


	Assist in applying discipline knowledge into student learning experiences to support success and engagement.
	Support innovation in curriculum design and deliver learner-centric, research-informed teaching practices across different levels / modes of delivery.
	Maintain deep discipline knowledge which is applied to inform curriculum design.
	Facilitate collaborative learning opportunities for students and provide academic mentoring and support
	Assist course teams in integrating new pedagogical research across all aspects of education.


To be successful, you’ll have: 


	PhD in a relevant discipline and/or other relevant qualifications and experience
	Demonstrated knowledge of anatomy and pathology and its delivery in an educational setting
	Excellent record of scholarly learning and teaching in UG and/or PG programs.
	Demonstrated capacity and passion for teaching, research and facilitation of student learning.
	Demonstrated capacity to provide academic support and guidance to students, ensuring an inclusive and positive learning environment.


Here's how to apply: 

Please submit your updated resume, a short cover letter and responses to the Key Selection Criteria.

For a confidential discussion regarding this role, please contact Janet McLeod, Associate Head of School (Learning and Teaching) on janet.mcleod@deakin.edu.au

Please note, Deakin university is closed for the Christmas period from Monday 23 December 2024 to Wednesday 1 January 2025 inclusive.

For a copy of the position description, please see below:  PD - Associate Lecturer in Medical Science.pdf

Applications for this position close 11.55pm on Monday 20 January 2025.

Why work with us?

At Deakin you will have access to benefits such as a variety of leave options including generous parental leave and the ability to purchase additional leave; flexible working arrangements to help manage your work-life balance; ongoing learning and development opportunities to grow your career; an inclusive and supportive culture and environment to work in, both online and on campus.

Are You Ready?

Deakin is a Victorian university with a global impact. We are an agile, dynamic, and innovative university committed to making a positive impact through our excellence in education, research and innovation and the contributions we make to the wider community.

We understand that our reputation has been built on the dedication and expertise of our staff and we offer a dynamic and diverse working environment with opportunities to grow and develop careers. We believe that a progressive, thriving culture will ensure that people choose to come, and stay at Deakin and contribute to our ongoing success.

We value diversity and aim to build an inclusive environment that champions, embraces and respects differences. We support and encourage applications from Aboriginal and Torres Strait Islander people, and people of all abilities, cultures, sexual orientation, and genders.


Deakin University

Singapore

The Singapore Institute of Technology (SIT) is Singapore’s first University of Applied Learning, offering industry-relevant degree programmes that prepare its graduates to be work- and future-ready professionals. Its mission is to maximise the potential of its learners and to innovate with industry, through an integrated applied learning and research approach, so as to contribute to the economy and society.

The university’s unique pedagogy integrates clinical practice education and academic endeavour, embracing authentic learning in a real-world environment through collaborations with key strategic partners. Its focus on applied research with clinical impact is aimed at helping the healthcare and social services sector innovate and grow.

As a young university, we would like to foster an academic environment that seamlessly integrates teaching, applied research and clinical practice. We offer competitive remuneration, generous employment benefits, access to funding to undertake research of relevance to industry, and opportunities to inspire and nurture the next generation of skilled allied health professionals. We currently have positions available at all levels, from Lecturer to Full Professor, for the discipline- Psychology in the Health and Social Sciences Cluster.

Applicants should possess:


	an earned PhD or equivalent from a reputable university in Psychology/ Clinical Psychology/Health Psychology, and
	research track record in the area of Psychology or Health Sciences.


The appointee will be expected to undertake funded clinical-relevant research, teach modules in SIT’s Health and Social Sciences undergraduate and/ or postgraduate programmes, as well as supervise students in applied learning and research activities. The appointee may also be provided with paid leave to undertake consultancy work with the healthcare industry and/or spend time attached with the industry.

Applications will be accepted until the positions are filled. All applications must be submitted electronically via the "Apply Now" button below. Please contact the Human Resource via HR_Faculty@singaporetech.edu.sg if you have any enquiries relating to this faculty position.


Singapore Institute of Technology (SIT)

FACULTY POSITIONS (AT ALL LEVELS) IN COMPUTER SCIENCE, COMPUTER ENGINEEERING AND INFORMATION SECURITY 

The Singapore Institute of Technology (SIT) is Singapore’s first University of Applied Learning, offering industry-relevant degree programmes that prepare its graduates to be work- and future-ready professionals. Its mission is to maximise the potential of its learners and to innovate with industry, through an integrated applied learning and research approach, so as to contribute to the economy and society.

The university’s unique pedagogy integrates work and study, embracing authentic learning in a real-world environment through collaborations with key strategic partners. Its focus on applied research with business impact is aimed at helping industry innovate and grow. Targeted to be ready in 2024, SIT’s centralised campus within the larger Punggol Digital District will feature a vibrant learning environment where academia and industry will be tightly integrated with the community.

SIT espouses an applied learning approach and offers degrees with a strong focus on science and technology. Our faculty work closely with industry on translational research and innovation, as well as provide consultancy on solutions to industry challenges. This well-established nexus with industry endows our graduates with deep skills and work-ready training experience – attuning them to the needs and demands of the future economy.

SIT is also establishing strategic live test beds known as ‘Living Labs’ both within and beyond its upcoming Punggol campus to support greater experiential learning and strengthen its applied research offerings. The university’s pool of ICT experts works with multi-disciplinary teams to drive the horizontal tech enablers required to support SIT’s strategic translational research.

As a young university, we encourage innovation and seek the right candidates to join us in realising our vision. We offer competitive remuneration, generous employment benefits, access to funding to undertake research of relevance to industry, and opportunities to inspire and nurture the next generation of skilled and work-ready graduates. We currently have positions available at all levels, from Lecturer to Full Professor, in the following areas: 


	Information Security: Mobile/distributed systems security, Cyber Physical Systems security, security analytics, digital forensics, vulnerability assessment, malware analysis and defence, Privacy-enhancing technologies, AI security.  
	Computer Science and Software Engineering: SDLC, requirement analysis, design, testing, optimisation, analysis, simulation, database, computer graphics, distributed systems, computer vision, video analytics  
	Emerging Fields: IIoT, computational fintech, robot-human interaction, Trustworthy AI, Explainable AI, Large Language Models, Quantum Computing.  
	We have established research teams in operational cybersecurity, AR/VR/HCI and immersive technologies, future transportation solutions, AI, future communications/5G, BCI and embedded systems. We welcome candidates who can complement or strengthen these areas.


Successful candidates will join the Infocomm Technology (ICT) Cluster (one of five academic clusters in SIT) as members of a team of accomplished experts who are passionate about higher education and deeply involved in industry transformation through applied research programmes.   

Faculty in the ICT Cluster undertake funded industry-relevant research, teach courses in Computer Science, Computer Engineering, Information Security and Software Engineering, as well as supervise graduate industry masters and doctorate students in relevant topics. They are also provided with paid leave to undertake consultancy work with industry and/or spend time attached with industry. 

We will prioritise collegial and team-spirited candidates with an earned PhD in a related discipline from a reputable university, plus relevant work experience or experience of industry-linked applied research projects.

Applications will be accepted until the positions are filled. All applications must be submitted electronically via the "Apply Now" button below. 


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A breakdown of student statistics at the University of Texas at Arlington

Key Student Statistics

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See below for a range of subjects taught at the University of Texas at Arlington

University of Texas at Arlington

Rankings

Arts and Humanities 2024501-600th

Business and Economics 2024501-600th

Clinical and Health 2024601-800th

Computer Science 2024301-400th

Education 2024401-500th

Engineering 2024251-300th

Life Sciences 2024601-800th

Physical Sciences 2024301-400th

Psychology 2024301-400th

Social Sciences 2024601-800th

Impact Rankings

Impact Rankings 2024201-300th

Zero Hunger 2024201-300th

Good Health and Wellbeing 2024101-200th

Quality Education 2024201-300th

Gender Equality 2024101-200th

Clean Water and Sanitation 2024301-400th

Decent Work and Economic Growth 2024401-600th

Industry, Innovation and Infrastructure 2024401-600th

Reduced Inequalities 2024101-200th

Sustainable Cities and Communities 202459th

Responsible Consumption and Production 2024301-400th

Climate Action 2024401-600th

Peace, Justice and Strong Institutions 2024201-300th

Partnership for the Goals 2024401-600th

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Key Student Statistics

Subjects Taught at the University of Texas at Arlington

Life Sciences.css-umjwst{margin-left:0.5rem;vertical-align:middle;font-size:0.75rem;color:#595959;}601–800th

Education401–500th

Arts and Humanities501–600th

Business and Economics501–600th

Clinical and Health601–800th

Computer Science301–400th

Engineering251–300th

Social Sciences601–800th

Psychology301–400th

Physical Sciences301–400th

Arts and Humanities 2024501-600^th

Business and Economics 2024501-600^th

Clinical and Health 2024601-800^th

Computer Science 2024301-400^th

Education 2024401-500^th

Engineering 2024251-300^th

Life Sciences 2024601-800^th

Physical Sciences 2024301-400^th

Psychology 2024301-400^th

Social Sciences 2024601-800^th

Impact Rankings 2024201-300^th

Zero Hunger 2024201-300^th

Good Health and Wellbeing 2024101-200^th

Quality Education 2024201-300^th

Gender Equality 2024101-200^th

Clean Water and Sanitation 2024301-400^th

Decent Work and Economic Growth 2024401-600^th

Industry, Innovation and Infrastructure 2024401-600^th

Reduced Inequalities 2024101-200^th

Sustainable Cities and Communities 202459^th

Responsible Consumption and Production 2024301-400^th

Climate Action 2024401-600^th

Peace, Justice and Strong Institutions 2024201-300^th

Partnership for the Goals 2024401-600^th

Life Sciences601–800^th

Education401–500^th

Arts and Humanities501–600^th

Business and Economics501–600^th

Clinical and Health601–800^th

Computer Science301–400^th

Engineering251–300^th

Social Sciences601–800^th

Psychology301–400^th

Physical Sciences301–400^th