Przemek Swatek, Phd, Cssbb Email & Phone Number

• Black Belt Six Sigma-certified Sr. Process Development Engineer with 9+ years of experience in high-tech development and academic research, utilizing scientific principles and advanced engineering practices. • Deep expertise in quantum physics, materials science, computational modeling, and statistical data analysis, evidenced by a patent, over 30 peer-reviewed publications, and the successful delivery of high-impact projects in various domains. • Proven track record of translating customer demands and business needs into engineering solutions, leading to significant enhancements in wafer-level product performance, reductions in material waste, and improvements in overall equipment effectiveness. • Skilled in effectively communicating complex scientific ideas to diverse project teams and clients, as shown through successful collaborations with liaisons from EMD, Intel, TSMC, and Seagate stakeholders. • Passionate about science, technology, and their link to well-being, as explored in my blog, "Physics of Your Mind". Hands-on lab/factory experience ➢ Microfabrication, photolithography, wet etching, test chip device design, Physical Vapor Deposition (PVD). ➢ Thin film/nanoparticles/device performance characterization: surface chemistry and morphology (XRD, XRF, AFM, TEM, SEDX/SEM, STEM), magnetic (VSM, MPMS, MOKEM/LOOPER, FMR, NMR, neutron scattering), spectroscopy (ARPES, Auger, XPS, AES, Raman, FTIR), thermodynamic (heat capacity, magnetostriction, thermal conductivity), low and high-frequency electrical measurements (GMR, 4-point probe, Hall effect, magneto transport, high mobility semiconductors), thermoelectric (Seebeck and Nernst effect). ➢ Ultra-high vacuum (UHV) cryogenics systems Modeling / Computing ➢ Compiling, installing, and scripting software on HPC systems combining C/C++, Fortran, and Bash language, Intel oneAPI Math Kernel Library, OpenMP, MPI, as well as accelerators Intel Xeon Phi and NVidia GPU. ➢ Computational atomic/physics/chemistry/mechanical modeling tools: DFT, DFPT, Molecular dynamics, Micromagnetic simulations, VASP, Quantum ESPRESSO, COMSOL Multiphysics, and SolidWorks. ➢ Working knowledge of Python (NumPy, Pandas), SQL (Oracle), R language, SAS JMP, Igor Pro, MATLAB, Mathematica, MS Office. ➢Strong understanding of machine learning algorithms and models, including deep learning, neural networks, decision trees, and k-nearest neighbors (TensorFlow and Keras). ➢ Familiar with Quantum Computing techniques in materials science using IBM Quantum and general Qiskit libraries.