Author: Gian Marco Paldino

The Laboratory of Translational Neuroanatomy and Neuroimaging (LN2T, https://ln2t.ulb.be/) and the Machine Learning Group (MLG, mlg.ulb.ac.be) 

of the Université libre de Bruxelles (ULB) are looking for a talented and motivated PhD student.

Candidates are invited to apply for a fully funded PhD position that will contribute to the design and assessment of novel machine learning techniques for the automatic detection of epileptiform discharges in magnetoencephalography (MEG) data. The successful candidate will benefit of a large database of MEG data acquired in epileptic patients and healthy subjects to develop machine learning models aiming at the automatic detection and localization of interictal epileptiform discharges. This position offers a unique opportunity to develop an interdisciplinary expertise in the fields of machine learning, MEG signal processing, and clinical MEG.

The position is funded for 4 years thanks to a research grant of the Fonds Erasme (Brussels, Belgium) https://fondserasme.org/fr

Expected starting date: 1st October 2024.

The successful candidate will work in a collaborative environment gathering two laboratories (LN2T and MLG) that are joining their expertise to conduct the proposed project. The LN2T is a multidisciplinary team composed of physicists, engineers, neuropsychologists, and neurologists. It holds a multimodal functional neuroimaging platform comprising a whole-scalp cryogenic MEG (Triux, MEGIN), an OPM-MEG facility, a MEG-compatible high-density EEG (Geodesic), and a hybrid PET-MRI system (3T MRI, Signa GE Healthcare). The ULB Machine Learning Group carries out theoretical research on statistical machine learning, time series forecasting and predictive modeling, and takes part to interdisciplinary research projects at international and national levels across diverse domains. In neuroscience, the MLG developed state-of-the-art methods in mental state classification, electrophysiological diagnostic tools (EDT) and prosthesis control. MLG is involved in several research initiatives at international and national levels: it is a  member of the CLAIRE confederation, a member of the TRAIL Walloon initiative on AI and of FARI, the Brussels AI institute.

Both labs (LN2T and MLG)  are on the ULB campuses in Brussels with easy access by public transports.

Candidates must hold a master’s degree (Bologna process) in engineering, computer science, physics, or a related discipline. A strong desire to learn about neuroscience and work in a multidisciplinary research environment is required. Strong proficiency in statistical programming languages (preferably Python and Matlab) is desirable. Preliminary experience in the field of machine learning or MEG/EEG signal processing is desirable but not required. French is not mandatory for the position.

Salary will correspond to the PhD grant of the ULB (around 2250-2400€/month for the PhD (net salary)) and should allow a comfortable life considering the living cost in Brussels.

Informal enquiries and applications can be addressed to Xavier De Tiège at xavier.de.tiege@ulb.be and Gianluca Bontempi at gianluca.bontempi@ulb.be.

Applications should comprise a detailed curriculum vitae (including programming skills), a letter of motivation, and the names of 2 reference persons.

Closing date: As soon as the position is filled.

March the 23th, 2023 at 3:00PM, on Teams: link.

In the latest years, scholars started focusing on how to develop statistical tool for the analysis of population of complex data, such as high-dimensional vector data and functions, but also more complex data objects such as functional time series or sets of graphs, either labelled or unlabelled. The present works adds to this literature by focusing on a strangely overlooked area, namely the formulation of prediction sets. By exploiting cutting edge techniques in the realm of machine learning, we propose a very powerful forecasting methodology, able to identify prediction regions in a very general sense, applicable to the great variety of possible data object the modern data scientist has to analyse. Our method, strongly based on Conformal Prediction, is model-free, achieves finite-sample validity, is computationally efficient and it identifies interpretable prediction sets, in the shape of a parallelotope. In the talk I will briefly present the basic ideas behind the methodology, its implementation to the functional and graph case, both labelled and unlabelled, as well as applications on real world data.

Matteo Fontana is a Project Officer at the Joint Research Centre of the European Commission, where is part of the Centre of Advanced Studies project “Computational Social Science for Policy”. From 2019 to 2021 he has been a Postdoctoral Researcher at the Modelling and Scientific Computing Lab of Politecnico di Milano, where he was involved in the development of an early warning system for geo-hazards in collaboration with the Italian Space Agency. He holds a PhD in Management Engineering from Politecnico di Milano, where he studied the application of novel statistical learning methodology to climate change economics research. He is a statistician/data scientist by training, he is mainly interested in applications of data science in economics, demography and migration studies. His main theoretical interests lie in the realm of nonparametric statistics (namely hypothesis testing and forecasting), as well as in the modelling of complex data objects.