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DISCnet Cohort 2021

Our students are exceptional problem solvers and highly-skilled data specialists.

From their PhD research they bring extensive experience of handling intensive tasks and managing complex data sets and problems to industry placements.

Meet The Students

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Anisa Aubin

University of Sussex

Working on Simfarm2030: An empirical, data driven, model for wheat cultivars and optimisation for future climate scenarios.

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Mauricio A. Diaz

University of Southampton

Machine Learning & Artificial Intelligence for BSM Phenomenology 

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Jussi Kuusisto

University of Sussex

Exploring Galaxy Formation in the Distant Universe with Hydrodynamical Simulations and Next Generation Space Telescopes"

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Francisco Martínez López

Queen Mary University London

Data Acquisition and Dark Matter studies at the DUNE FD.

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Callum Boocock

Queen Mary University London

Magnetohydrodynamic Waves in Realistic 3D Solar Atmospheric Structures

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Lewis Hill

University of Portsmouth

Stellar Populations in Galaxies with the Large Galaxy Survey SDSS IV/MaNGA".

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Joshua Wilde

The Open University

Using convolutional neural networks to identify gravitational lenses and working on interpreting these networks

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Giorgio Cerro

University of Southampton

Machine learning for particles jets

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Arran Freegard

University of Southampton

Exploring Dark Matter signals and their backgrounds with ATLAS at the LHC

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David Turner

University of Sussex

From Cluster Mass Calibration to Cluster Cosmology

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Jacob Orwat-Kapola

University of Southampton

Data science and machine learning applied to problems at the intersection of observational astronomy and the industry

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Adam McMaster

The Open University

Searching for black holes and variable stars in archival data from the SuperWASP survey, with help from citizen scientists and machine learning.

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Ryan Wood

University of Sussex

Jet Physics at the Large Hadron Collider in and beyond the Standard Model.

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Maria Del Carmen Campos Varillas

University of Sussex

Galaxy evolution: statistical analysis of data within the Herschel Extragalactic Legacy Project (HELP) 

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© 2024 by DISCnet 

DISCnet is the Data Intensive Science Centre in SEPnet, and an STFC Centre for Doctoral Training;  

a collaboration between the University of Sussex, Queen Mary University of London, and The Open University

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