Tensorflow
Tensorflow
Dr Elliot J. Crowley
We have a position for a postdoctoral research associate to work on NAS and AutoML with Dr Elliot J. Crowley and the Bayesian and Neural Systems Group. The successful applicant will be based in the School of Engineering at the University of Edinburgh, and will have opportunities for collaboration within and outside of the school e.g. with colleagues in the Institute for Digital Communications and the Bayesian and Neural Systems Group. This position is funded for 24 months (provisional start date: November 2023) and the salary is UE07 £36,333 - £43,155 Per Annum.
Xavier Hinaut
The main objectives of the internship will be: 1. to develop a graphical interface to train vocalization annotation models, to visualize their performance and to re-annotate parts of the dataset accordingly (in a similar fashion as semi-supervised learning); 2. to develop the corresponding software backend: data management (audio and annotations), serving and local persistence of the models (MLOps); 3. to collaborate with the project members to define the needs, establish the specifications or integrate pre-existing tools. This objective also implies collaborating with international researchers, and making an open source tool available to the public. The development will be incremental: a first prototype will allow to train models and to present their evaluation on the interface. A second prototype will offer advanced editing possibilities of the dataset (re-annotation of parts of the audio according to the results of the model), and the final version will integrate advanced analysis tools (dataset errors detection, spectrograms dimensionality reduction for visualization and/or clustering, syntactic analysis of song sequences, ...)
Prof. Aline Villavicencio
The successful candidate will be expected to lead the design and development of strategies for more transparent machine learning models to generate accurate cross-lingual representations for idiomatic language, as well as to contribute to the design and development of resources and evaluation of downstream tasks, like machine translation. For both lines of research, you will build on state-of-the-art approaches based on deep learning.
N/A
You will be working in the Pattern Analysis and Computer vision (PAVIS) Research Line, a multi-disciplinary and multi-cultural group where people with different backgrounds collaborate, each with their own expertise, to carry out the research on Computer Vision and Artificial Intelligence. PAVIS research line is coordinated by Dr. Alessio Del Bue. Within the team, your main responsibilities will be: Hardware and software prototyping of computational systems based on Computer Vision and Machine Learning technology; Support PAVIS facility maintenance and organization; Support PAVIS Technology Transfer initiatives (external projects); Support PAVIS researcher activities; Support PAVIS operations (procurement, ICT services, troubleshooting, data management, logistics, equipment management and maintenance).
Joël Ouaknine
The successful candidate will work in close collaboration with academic and industrial partners, delving deep into the verification of Large Language Models (LLMs) based software programs. The focus of the position includes designing and implementing innovative verification methods to ensure the reliability and accuracy of LLM-based software programs, and actively engaging in the design and development of a system that generates high-quality data utilising LLMs. The project involves establishing methods to validate the efficacy of the prompting processes in getting accurate responses from LLMs and developing strategies to verify the overall reliability of the LLM-based software program. The postdoctoral researcher will further refine this approach, aiding in the development of a system optimised for high-quality data generation using LLMs. The successful candidate is expected to spend one or more internships in industry and liaise with industrial partners.
Francesco Piccialli
The main objective of the research activity will be the design and application of advanced Machine Learning and Deep Learning methodologies for data analysis in the context of a Smart City and Digital Society. The aim is to develop predictive models and intelligent systems capable of extracting meaningful information from the data collected within a Smart City, enabling optimized resource management, improving the quality of life for citizens, and promoting a more effective and sustainable digital society. The tasks include performing cutting edge research on Machine and Deep Learning methodologies for Smart City, working on the project, especially planning, implementing and executing research, conducting and participating in research projects such as lab and equipment set up, data collection, data analysis, participating in routine laboratory operations, such as planning and preparations for experiments, lab maintenance and lab procedures, and coordinating with the PI and other team members for strategies and project planning.
Sebastiano Vascon
The selected candidate will work on a project of national interest on Computer Vision applied to Robotics for Health. The aim is to develop an active assistive device (walker) for people with walking deficits. The project involves three partners, Ca' Foscari University of Venice, the University of Padova, and the University of Catania, and several technologies. The candidate will be expected to actively contribute to the laboratory activities by participating in weekly seminars, discussions, and research-related tasks.
Prof. Gang Luo
The postdoctoral fellow will work on topics of mutual interest such as, but not limited to, automatic machine learning model selection and automatically explaining machine learning classification / prediction results. The initial appointment is for one year with the expectation of extension given satisfactory performance.
Sebastiano Vascon
The selected candidate will work on a project of national interest on Computer Vision applied to Robotics for Health. The aim is to develop an active assistive device (walker) for people with walking deficits. The project involves three partners, Ca' Foscari University of Venice, the University of Padova, and the University of Catania. The candidate will be expected to actively contribute to the laboratory activities by participating in weekly seminars, discussions, and research-related tasks.
N/A
We seek to appoint a full-time Machine Learning Research Engineer to contribute to the development of new technologies for cutting edge vision systems in the context of an industrial project in collaboration with a large multinational company. The project carries out innovative research on the topics of Visual Question Answering and fast adaptation of vision-language models. The project team will be responsible for all the phases of the research development, including methods design and implementation, data preparation and benchmarking, task planning and frequent reporting. Within the team, your main responsibilities and duties will depend on your expertise and experience.
Brandon (Brad) Minnery
We currently have an opening for a full-time Senior Human-Computer Interaction Researcher whose work seeks to incorporate recent advances in generative large language models (LLMs). Specific research areas of interest include human-machine dialogue, human-AI alignment, trust (and over-trust) in AI, and the use of multimodal generative AI approaches in conjunction with other tools and techniques (e.g., virtual and/or augmented reality) to accelerate learning in real-world task environments. Additional related projects underway at Kairos involve the integration of generative AI into interactive dashboards for visualizing and interrogating social media narratives. The Human-Computer Interaction Researcher will play a significant role in supporting our growing body of work with DARPA, Special Operations Command, the Air Force Research Laboratory, and other federal sponsors.
N/A
PAVIS is looking to strengthen its activities on 3D multi-modal scene understanding. The research will focus on novel ML and CV methods that efficiently incorporate priors and constraints from world physical models and semantic priors, derived from vision, language models, or other modalities. The project will explore the interplay between vision and large language models to address tasks in 3D reasoning, visual (re-)localization, active vision, and neural/geometrical novel view rendering. The aim is to develop models applicable to interdisciplinary research, including drug discovery and robotics, utilizing in-house robotics platforms and HPC computational facilities.
Prof. Joschka Boedecker
Full-time PhD positions on planning and learning for automated driving at the Neurorobotics Lab, University of Freiburg, Germany. The project involves working in a team with excellent peers in a larger project with an industry partner.
Hakan Bilen
The successful candidate will have an opportunity to work on cutting-edge computer vision and machine learning research projects. The goal of this project is to synthesising anonymised training datasets.
Louis Marti
We currently have an opening for a full-time Senior Human-Computer Interaction Researcher whose work seeks to incorporate recent advances in generative large language models (LLMs). Specific research areas of interest include human-machine dialogue, human-AI alignment, trust (and over-trust) in AI, and the use of multimodal generative AI approaches in conjunction with other tools and techniques (e.g., virtual and/or augmented reality) to accelerate learning in real-world task environments. Additional related projects underway at Kairos involve the integration of generative AI into interactive dashboards for visualizing and interrogating social media narratives. The Human-Computer Interaction Researcher will play a significant role in supporting our growing body of work with DARPA, Special Operations Command, the Air Force Research Laboratory, and other federal sponsors.
Prof. Dr. Yee Lee Shing, Prof. Dr. Gemma Roig
The DFG funded project Learning From Environment Through the Eyes of Children within SPP 2431 New Data Spaces for the Social Sciences, situated at Goethe University Frankfurt, is looking for candidates for two positions: 1 PostDoc position in Psychology and 1 PhD or PostDoc position in Computer Science. The project aims to establish a new mode of data acquisition capturing young children’s first-person experience in naturalistic settings and develop AI systems to characterize the nature and complexity of these experiences. This interdisciplinary project involves collaboration between the psychology and computer science departments, contributing to the SPP programme's goals of establishing a new multimodal data approach in social science studies.
N/A
We are hiring two PostDocs to join the Pattern Analysis and Computer Vison (PAVIS) Research Line coordinated by Dr. Alessio Del Bue. The positions are part of a joint multidisciplinary effort between PAVIS, the Atomistic Simulation (ATSIM) Research Line led by Prof. Michele Parrinello, the Computational Facility led by Dr. Sergio Decherchi and Dompè Pharmaceutical. The focus is on the study of novel ML and DL methods that can efficiently incorporate priors and constraints related to physical models, with special focus on self-supervised and generative modelling. The goal is to develop models applicable to IIT interdisciplinary research, especially in drug discovery and molecular modeling for large scale datasets, leveraging IIT’s HPC computational facilities.
N/A
The successful candidate will work to develop specific solutions within the European project XTREME for: Mapping multichannel sound to an acoustic image stream with beamforming; Multimodal audio-visual detection and fusion for scene representation; Integration of 2D audio-visual reconstructed scene with 3D representations.
Martin Krallinger, Dr.
The Natural Language Processing for Biomedical Information Analysis (NLP4BIA) group at BSC is an internationally renowned research group working on the development of NLP, language technology, and text mining solutions applied primarily to biomedical and clinical data. It is a highly interdisciplinary team, funded through competitive European and National projects requiring the implementation of natural language processing and advanced AI solutions making use of diverse technologies, including Transformers and recent advances in Large Language Models (LLM) to improve healthcare data analysis. The NLP4BIA-BSC is looking for a Research Engineer with experience in Language Technologies and Deep Learning. The candidate will be involved in technical work related to international projects, being part of a team of researchers working on topics related to clinical Language Models, multilingual NLP, benchmarking of language technology solutions and predictive content mining. The candidate will have the opportunity to advance the state of the art of biomedical language models and NLP methods working in a multidisciplinary environment alongside AI experts, computational linguists, clinical experts, and other engineers.
Joël Ouaknine
We invite applications for a postdoctoral research position in the Foundations of Algorithmic Verification group led by Prof. Joël Ouaknine. The successful candidate will work in close collaboration with an industrial partner, delving deep into the verifications of Large Language Models (LLMs) based software programs, and contributing to bridging scientific research and applications. The project aims to develop reliable LLM-based data curation systems for data verification and data enrichment tasks such as verifying or discovering entity relationships from textual documents and/or the Web. The postdoctoral researcher will contribute to defining the methodology and develop and refine this approach, assisting in the development of a system optimized for data curation using LLMs. The position focuses on research and development of innovative verification methods to ensure the reliability and accuracy of LLM-based data curation programs and actively collaborating with industrial partners.
Jie Mei
The Wiring, Neuromodeling and Brain Lab at IT:U Interdisciplinary Transformation University Austria is offering 2 PhD positions in neuromodulation-aware artificial intelligence. We are interested in (1) the role of individual neuromodulators (e.g., dopamine, serotonin, and acetylcholine) in initiating and implementing diverse biological and cognitive functions, (2) how competition and cooperation among neuromodulators enrich single neuromodulator computations, and (3) how multi-neuromodulator dynamics can be translated into learning rules for more flexible, robust, and adaptive learning in artificial neural networks.
Georgios Exarchakis
The University of Bath invites applications for a fully-funded PhD position in Machine Learning, as part of the prestigious URSA competition. This project focuses on developing interpretable machine learning methods for high-dimensional data, with an emphasis on recognizing symmetries and incorporating them into efficient, flexible algorithms. This PhD position offers the opportunity to work within a leading research environment, using state-of-the-art tools such as TensorFlow, PyTorch, and Scikit-Learn. The research outcomes have potential applications in diverse fields, and students are encouraged to bring creative and interdisciplinary approaches to problem-solving.
Jagath Rajapakse
Two post-doctoral research positions are available in AI-inspired drug design in the Biomedical Computing Group headed by Professor Jagath Rajapakse at Nanyang Technological University, Singapore, for a period of three years starting from 1 July 2025. The project investigates the design of biologics (peptides and antibodies) as anticancer therapeutic agents by using eXplainable AI (XAI) and generative AI (genAI). First, we build predictive AI models such as large language models (LLM) for predicting binding affinities of biologics. Second, we use XAI approaches such as integrated gradients for identifying the features of predictive models and the mechanism of action of biologics. Third, using these features as constraints, we will use genAI techniques such as LLM and diffusion models to generate biologics with anti-cancer properties. The candidate will develop necessary predictive AI, XAI and genAI methods for design of anti-cancer biologics.
Efficient GPU training of SNNs using approximate RTRL
Last year’s SNUFA workshop report concluded “Moving toward neuron numbers comparable with biology and applying these networks to real-world data-sets will require the development of novel algorithms, software libraries, and dedicated hardware accelerators that perform well with the specifics of spiking neural networks” [1]. Taking inspiration from machine learning libraries — where techniques such as parallel batch training minimise latency and maximise GPU occupancy — as well as our previous research on efficiently simulating SNNs on GPUs for computational neuroscience [2,3], we are extending our GeNN SNN simulator to pursue this vision. To explore GeNN’s potential, we use the eProp learning rule [4] — which approximates RTRL — to train SNN classifiers on the Spiking Heidelberg Digits and the Spiking Sequential MNIST datasets. We find that the performance of these classifiers is comparable to those trained using BPTT [5] and verify that the theoretical advantages of neuron models with adaptation dynamics [5] translate to improved classification performance. We then measured execution times and found that training an SNN classifier using GeNN and eProp becomes faster than SpyTorch and BPTT after less than 685 timesteps and much larger models can be trained on the same GPU when using GeNN. Furthermore, we demonstrate that our implementation of parallel batch training improves training performance by over 4⨉ and enables near-perfect scaling across multiple GPUs. Finally, we show that performing inference using a recurrent SNN using GeNN uses less energy and has lower latency than a comparable LSTM simulated with TensorFlow [6].