Piotr Mieczkowski

Programmer - scientist - biophysicist

Python, C++, Django, data analysis, researcher in physics and biology

More About Me

Currently, I am working for Thales corporation. My task regards data processing using Samarcande software and Python. In my spare time, I keep busy doing projects for future students in JetBrains Hyperskill.

In recent years, I mainly developed myself in Python and related things, such as data analysis, Django, machine learning and bioscience. I am looking for a full-time job that will allow me to develop any of those skills. In my scientific research, I like cancer research and all other biophysical problems.

My education is mainly based on physics especially in computing and molecular physics. My MSc topic relates to quantum physics and advanced mathematical computations. On the other hand, my bachelor based on molecular physics and analysis of spectrometry results.

Programming languages


2 years of commercial and non-commercial work. Nowadays, I work for Thales in data processing and cooperate with JetBrains - Hyperskill company.


1 year of non-commercial work.

Java Script

Essential minimum to create interesting websites.


Working with language during over 4 years of scientific work.


Basic knowledge.

Other Skills

mathematical calculus
moderate biology level
JS Chart

Soft Skills

work in international team
work in interdisciplinary project
analytical mind

Scientific carrier

Master of Philosophy Thesis


Engineered tissues have many potential applications both as experimental systems and in therapeutic devices and treatments. Specially, there is potential to repair damaged spinal cord, however work is still needed to predict the process of tissue growth. Computational physics has potential to significantly accelerate the progress of development and to improve the properties of engineered neural tissue (ENT). Research has found that correlation exists between physical forces and the arrangement of cells in engineered tissue. In this thesis, emphasis is put on the interaction between atrocities within engineered neural tissue (ENT). The main aim of this work is to create a computational model which will be able to predict the arrangement of these cells in ENT. Computational techniques of soft matter physics are used to describe the final growth state of cells in ENT. In this thesis, we treat tissue as a two dimensional mesh. Interactions are described by both a simple Hamiltonian related to cell orientation and the dipole force model (DFM) and a more advanced Hamiltonian, which takes into account the influence of the extracellular matrix and cells. To find the minimum energy state of the system a simulated annealing algorithm was used. The first set of results presents a two-dimensional map of real artificial engineered neural tissue and shows the statistical distribution of the average angles and positions of groups of cells within the tissue. Next, we present results generated by a simple Hamiltonian and more advanced model to investigate information about orientation, arrangement and shape of the whole tissue. It is found that an extended DFM where cells can move and change orientation is able to reproduce several features of the experimental data including an approximation to the delta regions and the aligned behaviour in the centre of the sample.

The article


Microscopic biophysical model of self-organization in tissue due to feedback between cell- and macroscopic-scale forces.


We develop a microscopic biophysical model for self-organization and reshaping of artificial tissue, that is codriven by microscopic active forces between cells and an extracellular matrix (ECM), and macroscopic forces that develop within the tissue, finding close agreement with experiment. Microscopic active forces are stimulated by μm -scale interactions between cells and the ECM within which they exist, and when large numbers of cells act together these forces drive, and are by, macroscopic-scale self-organization and reshaping of tissues in a feedback loop. To understand this loop, there is a need to (1) construct microscopic biophysical models that can simulate these processes for the very large number of cells found in tissues, (2) validate and calibrate those models against experimental data, and (3) understand the active feedback between cells and the matrix, and its relationship to macroscopic self-organization and reshaping of tissue. Our microscopic biophysical model consists of a contractile network representing the ECM, that interacts with a large number of cells via dipole forces, to describe macroscopic self-organization and reshaping of tissue. We solve the model using simulated annealing, finding close agreement with experiments on artificial neural tissue. We discuss the calibration of model parameters. We conclude that feedback between microscopic cell-ECM dipole interactions and tissue-scale forces is a key factor in driving macroscopic self-organization and reshaping of tissue. We discuss the application of the biophysical model to the simulation and rational design of artificial tissues.