Open to opportunities

Fabio Covito

@fabiocovito

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Computational physicist and scientific software engineer applying ML and HPC for reliable simulations.

Germany

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What I'm looking for

I’m looking to build and scale scientific software with strong HPC performance and practical ML/LLM workflows, in collaborative research teams. I want roles where benchmarking, automation, and reliability directly drive scientific outcomes.

I’m a computational physicist and scientific software engineer with 10+ years of experience building high-performance simulations and performance-driven algorithms. I focus on designing, building, and maintaining robust scientific software that performs under real research constraints, with clear emphasis on profiling, benchmarking, and resource optimization.

At HQS Quantum Simulations, I’ve led development efforts ranging from condensed-matter research software to AI-enabled scientific workflows. I’m one of the two creators of the generative Modeling Assistant framework and led a team of four to deliver an LLM-based tool that interfaces with scientific software, built on published methods (EP4621531A1). I’ve also driven major performance improvements through work on Qolossal, and built ML pipelines for automated structural elucidation from NMR spectroscopy.

Alongside scientific computing, I apply machine learning to optimization and data-driven forecasting. My experience includes developing Monte Carlo optimization workflows for parameter calibration and, earlier, structuring data pipelines and building boosted-trees ML systems for advertisement impact and market revenue prediction. I thrive at the intersection of science and software engineering—turning complex requirements into reliable systems delivered in collaborative, research-driven environments.

Experience

Work history, roles, and key accomplishments

Current

Lead Developer – AI

Current

HQS Quantum Simulations

Jan 2024 - Present (2 years 6 months)

Co-created the Modeling Assistant, a generative AI framework, and coordinated a team of four to develop it. Developed ML pipelines using CNNs for automated structural elucidation of organic molecules from NMR spectroscopy.

Generative AI ML Pipelines CNN NMR Structure Elucidation Python CI CD Testing And QA Docker Git

Current

Senior Expert – Condensed matter systems

Current

HQS Quantum Simulations

Jul 2023 - Present (3 years)

Main developer of Qolossal, a large-scale linear scaling tight-binding solver. Achieved a 5x speed-up with reduced memory usage and improved numerical stability, and built a Monte Carlo optimization workflow for parameter calibration to match experimental NMR spectra.

Linear Scaling Tight Binding Solvers Algorithm Design Performance Optimization Numerical Stability Scientific Computing NMR

Current

Many-body Theory Specialist

Current

HQS Quantum Simulations

Jun 2023 - Present (3 years 1 month)

Conducted research and development of linear scaling methods for condensed matter physics. Implemented efficient numerical algorithms for calculating material properties and received recognition via a technical improvement proposal.

Condensed Matter Physics Simulation Material Properties Modeling Scientific Computing Algorithm Design

Data Scientist

SimCog Technologies GmbH

Oct 2020 - Mar 2021 (5 months)

Structured data pipelines for analysis of advertisement impact. Built boosted-trees machine learning systems to predict market revenue behavior.

Data Pipelines Machine Learning Workflows Boosted Trees Forecasting Predictive Analytics

Postdoctoral Researcher

University of Hamburg and Max Planck

Apr 2020 - Jul 2020 (3 months)

Developed ab-initio simulation capabilities for correlated charge dynamics in molecules. Created analytical methods using mathematical modeling and programming.

Ab Initio Simulation Development Correlated Charge Dynamics Mathematical Modeling Programming

Education

Degrees, certifications, and relevant coursework

Max Planck Institute for Structure and Dynamics of Matter

Ph.D., Physics

2015 - 2020

Ph.D. studies in Physics focusing on an efficient ab-initio non-equilibrium Green's function approach to carrier dynamics in many-body interacting systems.