Anic Anna

• Modeled advanced electro-optical sensors to ensure highly accurate prediction of system behavior and performance, consistently delivering results ahead of schedule with exceptional precision.
• Evaluated, optimized, and implemented automated image recognition algorithms, enabling robust performance assessment of both existing and self-designed Counter drone subsystems and complete integrated solutions.
• Actively contributing to a patent filing for an innovative invention, demonstrating strong capability in turning research into tangible intellectual property (DE 10 2024 101 577 Al).
• Designed and co-developed an AI validation framework to rigorously benchmark the efficiency and reliability of object-recognition algorithms, significantly enhancing operational trustworthiness.
• Engineered and simulated air-defense scenarios for large-scale data generation, supporting the training of advanced reward models for reinforcement learning applications.
• Conceptualized and built automated evaluation pipelines to analyze and benchmark the effectiveness of diverse simulated air-defense architectures, streamlining assessment processes and boosting efficiency.
• Authored and presented multiple technical studies to senior stakeholders, ensuring actionable insights and clear decision-making support.
• Selected into the Talent Pool 2023, recognized as a high-potential engineer with exceptional performance, rapid learning ability, and strong interdisciplinary expertise.

• Detection of potential patent infringements, including the preparation of in-depth infringement analyses. Designed and proposed a licensing negotiation strategy to maximize value creation. Selected participant in the highly competitive Siemens Future Minds Student Program.

• Facilitated interactive problem-solving sessions for Molecular Modeling and Statistical Thermodynamics

• Engineered an automated benchmarking framework optimizing the evaluation of parallel scalability for quantum chemistry applications on high-performance computing clusters.

Master’s Thesis – Chair of Theoretical Chemistry
“Ab-Initio Molecular Dynamics of Electrode–Electrolyte Interfaces”

Developed a simulation protocol for ab-initio molecular dynamics to investigate electrode–electrolyte interfaces under realistic conditions. Combined density functional theory for electronic structure calculations with large-scale molecular dynamics to explore the impact of interfacial water configurations on electrochemical properties. Generated and analyzed high-dimensional datasets capturing structural, electronic, and dynamic features at the atomistic scale. This integrative framework enabled a data-driven understanding of interfacial phenomena, linking electronic structure theory with molecular dynamics to advance predictive modeling in electrochemistry.
GPA: 1.0 (highest distinction)

Bachelor’s Thesis – Chair of Theoretical Biophysics
“Molecular Dynamics Investigation of DNA Duplex Formation”

Designed and executed large-scale molecular dynamics simulations to study the reversible denaturation and re-formation of DNA double strands. Modeled diverse sequenced DNA fragments under multiple implicit solvent conditions, generating datasets on conformational transitions. Applied statistical and thermodynamic analyses to extract kinetic rates, structural features, and melting temperatures, benchmarking results against theoretical models and experimental data. This work not only provided atomic-level insights into DNA stability but also contributed to frameworks that bridge molecular simulations, biophysical observables, and large-scale computational modeling.
GPA: 1.0 (highest distinction)