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👋 Hi, I'm Jake Jackson welcome to my GitHub

I’m a Physics of Data Masters graduate working in a biomedical research position exploring intrinsically disordered protein interactions through the application of machine learning and physics-based insights. I enjoy applying novel datascience techniques to help overcome technical limitations and enable new science.

•Biomedical Researcher 🔬 • Physics of Data MSc at UNIPD 🇮🇹 • MPhys Physics Uni of Liverpool 🇬🇧

• Data Scientist 🧑‍💻 • Rock Climber 🧗‍♂️ • Web Developer 🖱️

Key Projects

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I'm experienced with...

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📫 Reach me on [email protected]

Jake Jackson's Projects

interestingdatascienceprojects icon interestingdatascienceprojects

This repository is here to host the finalised versions of various data science projects that I have competed. This to showcase my capabilities for future employers or collaborators.

laboratoryofcomputationalphysics icon laboratoryofcomputationalphysics

My homework and projects for Laboratory of Computational Physics. This course is centred around implementing Machine Learning and computer science techniques on scientific problems.

laboratoryofcomputationalphysicsb icon laboratoryofcomputationalphysicsb

This repo contains Machine Learning weekly projects and a "Physics of data" research project applying these techniques in Astrophysics to give insight into Binary Black Hole Time Delay

mapd icon mapd

FPGA project in VHDL for MAPD at UNIPD

mapdb icon mapdb

MAPBD: This repo has many exercises on databases, distributed computing, and ML. In addition, it also contains our Dask Distributed CNN Sea Turtle Project.

molecular_simulations icon molecular_simulations

This repo contains the tools and personal resources for the 2023 molecular simulations course.

r_advanced_stats icon r_advanced_stats

This repository contains my homeworks and functional programming in R was developed through the UNIPD Advanced Statistics course.

thesis_normalizing_flows_for_binary_black_holes icon thesis_normalizing_flows_for_binary_black_holes

UNIPD Physics of Data Thesis. Using the Normalising Flows machine learning technique to understand the conditional probabilities of key parameters the resulted in merging binary black holes in the population synthesis code SEVN.

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