04 Postdoctoral Fellowship at University of Sussex, East Sussex, United Kingdom

A post-doctoral position is available in the laboratory of Leon Lagnado to study the flow of information controlling visually-driven behaviour.  The project involves a combination of experiments and modelling.  Fluorescent reporters of neural activity will be used in conjunction with advanced imaging methods and behavioural assays to investigate how the signals transmitted through different visual pathways in larval zebrafish determine behavioural outputs such as the optomotor response, prey capture and escape response.  We are particularly interested in how neuromodulators adjust the efficiency of information transmission in early visual processing and how this impacts these behaviours.  

This is an academic role, where you will conduct a specified programme of research supported by research training and development under the supervision and direction of a Principal Investigator. 

The primary purpose of the role is to further develop your research skills and competence, including the processes of publication in peer-reviewed academic publications and the development of funding proposals.

Transformation of the global food system towards more sustainable and equitable outcomes is urgently needed, requiring an evidence-based food systems approach. The Co-Centre for Sustainable Food Systems aims to transform the existing food systems in Ireland and the UK, addressing economic, social, health and environmental problems to ensure safe nutritious food for all. Our research will consider specific challenges addressing food system integrity and resilience, food safety and healthy diets from sustainable sources. Using a food systems approach, the Co-Centre for Sustainable Food Systems encompasses a research programme of oriented basic and applied research in the areas of science, technology, engineering and social sciences across three jurisdictions – the Republic of Ireland, Northern Ireland, and Great Britain. 

Applications are invited for the position of a Research Fellow in Trapped Ion Quantum Computing in the Ion Quantum Technology Group in the Sussex Centre for Quantum Technologies at the University of Sussex. The position is part of the UK National Quantum Technologies programme and is concerned with the development of utility scale quantum computers capable of solving disruptive societal and industry challenges.

Trapped ions are one of the most mature implementations to deliver practical quantum technologies. We have developed a unique approach for trapped-ion quantum computing that enables scaling to a large number of qubits. The approach couples scalable global microwave based quantum gate technology with fully integrated quantum computing microchip modules that can be linked together using fast electric field links.

This position is part of a larger effort to develop and operate quantum computing solutions which scale to significantly larger system sizes. We currently operate 5 quantum computer prototypes in our lab and the position will focus on one or multiple of these experimental setups directed towards the implementation of quantum error correction, ion transport and quantum gate execution, raising gate fidelities, operation of multi-module quantum computing prototypes and the development of quantum computing microchips.

Applications are invited for a Research Fellow in Machine Learning at the Predictive Analytics Lab (https://wearepal.ai/) in the Department of Informatics at the University of Sussex. The laboratory is currently supported by 3 active EU grants: BayesianGDPR (ERC), TANGO (EU Horizon), and Act.AI (ERC Proof of Concept). 

The position is part of the ERC funded project “Bayesian Models and Algorithms for Fairness and Transparency” (BayesianGDPR) – led by Professor Novi Quadrianto. It involves the development of novel inference and computational methods towards the realisation of fair and transparent machine learning systems in static and dynamic settings. 

The successful applicant should have a PhD in machine learning in a field relevant to our research area such as active learning, online learning, stochastic optimisation along with a good publication record in leading machine learning publication venues. The salary offered will be appropriate to the qualifications, standing and experience of the successful candidate.