Computational Scientist
Hussen Oumer
PhD Student at DIPC · Soft Matter & Ionic Liquids · Transitioning to AI-driven R&D. Managing 2.5M CPU hours at BSC.
01 — About
Who I am
I'm Hussen Oumer Mohammed, a PhD student and early-stage researcher at the Donostia International Physics Center (DIPC), based in San Sebastián, Spain. My work sits at the frontier of computational chemistry, soft condensed matter, and machine learning.
My doctoral research focuses on the computational design of draw solutes for Forward-Osmosis seawater desalination — using molecular simulation and data-driven methods to engineer next-generation water-treatment materials.
In parallel I manage 2.5 million CPU hours at the Barcelona Supercomputing Center (BSC), where I coordinate large-scale simulation campaigns on ionic liquids (TRILs/ILs) and their thermodynamic behaviour, including LCST phenomena. I'm now actively transitioning toward AI-driven R&D — integrating deep learning with domain physics to accelerate materials discovery.
PhD Student — DIPC, San Sebastián
Computational Design · Forward-Osmosis Desalination · 2023 – present
HPC Resource Manager — BSC Barcelona
2.5M CPU-hour allocations · Large-scale IL/soft-matter simulations
Researcher — Ionic Liquids & Soft Matter
LCST-IL phase behaviour · Monte Carlo methods · Fortran & Python
Hussen Oumer Mohammed
PhD Student · Computational Scientist
02 — Research
Research focus
Computational Design of Draw Solutes for Forward-Osmosis Desalination
Molecular dynamics and Monte Carlo simulation of candidate draw solutes to engineer high-performance, low-energy seawater desalination membranes using forward-osmosis principles.
Learn more ›LCST Behaviour of Ionic Liquids (IL Simulations)
Large-scale HPC simulations exploring lower critical solution temperature (LCST) phase transitions in ionic liquids, combining thermodynamic modelling with Fortran-based Monte Carlo engines.
GitHub repo ›Thermodynamic Properties of Task-specific ILs (TRILs)
Systematic computational study of task-specific room-temperature ionic liquids (TRILs), characterising structural, dynamic, and thermodynamic properties relevant to separation processes.
GitHub repo ›2D Monte Carlo Simulation Engine
High-performance 2D Monte Carlo simulation code written in Fortran for studying soft-matter and colloidal systems at the mesoscale. Designed for BSC HPC deployment.
GitHub repo ›Scientific Python Utilities (scicomp-py)
A collection of Python utilities for scientific computing, post-processing simulation outputs, data analysis, and visualisation for computational physics workflows.
GitHub repo ›AI-Driven Materials Discovery
Transitioning computational expertise toward machine-learning-assisted materials screening — using neural network potentials and data-driven surrogate models to accelerate simulation-based discovery.
Collaborate ›03 — Skills
Technical expertise
Simulation methods
Programming
HPC & Software
Research & Communication
04 — Projects
Open-source work
scicomp-py
Scientific Python utilities for post-processing simulation outputs, statistical analysis, and publication-quality visualisation of MD / MC data.
2D Monte Carlo Simulation
High-performance 2D MC engine in Fortran 90 for soft-matter and colloidal systems. Optimised for deployment on BSC HPC clusters via SLURM.
TRILs — Task-specific ILs
Simulation data and analysis scripts for task-specific room-temperature ionic liquids. Includes thermodynamic characterisation and structural analysis pipelines.
LCST-IL-Simulations
Simulation workflows and scripts for LCST phase-transition studies in ionic liquids. Full BSC HPC pipeline from submission to analysis.
Personal Academic Website
This portfolio — built with pure HTML & CSS, deployed via GitHub Pages. Inspired by the interactive academic site style of Kiarash Farajzadehahary.
05 — Contact
Get in touch
Whether you're interested in collaborating on computational materials science, AI-driven R&D, or just want to connect — my inbox is always open.
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