Marwan Mostafa

M.Sc.
Research Assistant

Contact

Marwan Mostafa, M.Sc.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Office Hours
nach Vereinbarung/ by appointment
Harburger Schloßstraße 36,
21079 Hamburg
Building HS36, Room C3 0.013
Phone: +49 40 42878 4097
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Research Project

iNeP
Integrated network planning for the electricity, gas and heat sectors

iNeP

Integrated network planning for the electricity, gas and heat sectors

Federal Ministry for Economic Affairs and Climate Action (BMWK); Duration: 2021 to 2026

Publications

TUHH Open Research (TORE)

2023

2022

2021

Courses

Stud.IP
link to course in Stud.IP Studip_icon
Machine Learning in Electrical Engineering and Information Technology
Semester:
SoSe 24
Course type:
Lecture
Course number:
lv3004_s24
Lecturer:
Prof. Dr. sc. techn. Christian Schuster, Prof. Dr.-Ing. Christian Becker, Prof. Dr. Alexander Kölpin, Gerhard Bauch, Dr. Maximilian Stark, Dr. Davood Babazadeh, Dr. Cheng Yang, PD Dr.-Ing. habil. Rainer Grünheid, Simon Stock, M.Sc.
Description:
This master course, a collaborative effort between the Institute of Communications, the Institute for High-Frequency Engineering, the Institute for Power Systems, and the Institute for Theoretical Electrical Engineering, is designed to unveil the synergies between machine learning and our respective fields of expertise. In an age defined by rapid technological advancement, machine learning stands as a catalyst for innovation, offering transformative possibilities across diverse sectors. From optimizing communication systems to enhancing power grid efficiency, and from refining signal processing techniques to enabling autonomous systems, the integration of machine learning techniques holds immense promise for addressing contemporary challenges. Throughout this course, we will delve into the theoretical underpinnings, practical methodologies, and tangible applications of neural networks and machine learning algorithms. By delving into algorithmic design, data analysis, and optimization techniques, we aim to equip you with the skills and insights needed to navigate the complexities of modern engineering landscapes.
Performance accreditation:
m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul>
ECTS credit points:
6
Stud.IP informationen about this course:
Home institute: Institut für Nachrichtentechnik (E-8)
Registered participants in Stud.IP: 102
Postings: 3
Documents: 23

Supervised Theses

ongoing
completed

2022

  • Barthelme, J. (2022). Technisch-ökonomische Systemmodellierung und -anlayse eines urbanen Quatiers hinsichtlich des Einsatz von Wasserstoff als primärer Energieträger.