Hello, I’m

Naim Shareq

Quantitative Analyst · Applied Mathematician · Energy Markets

MSc student at the University of Bremen building mathematical models, forecasting systems, and data-driven tools for energy trading.

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About

I am an applied mathematician focused on energy forecasting, optimisation, machine learning, and scientific computing. I enjoy turning imperfect real-world datasets into reliable models and clear decisions.

My current work spans load, wind, and solar profile modelling, energy-market data quality, forecasting workflows, and the migration of analytical models into production-ready services. My academic interests include online algorithms, numerical methods, PDEs, and data-driven optimisation.

Experience & education

Experience

May 2026 — Present

Working Student, Analysis Team

EWE Trading

Bremen, Germany

Develop Python-based energy forecasting and data-processing workflows.
Build and validate load, wind, and solar power profiles.
Analyse energy-market data and investigate data-quality issues.
Support the migration of legacy models into production-ready services.
Create dashboards and visualisations for forecast comparison.

Experience

Apr 2025 — Mar 2026

Student Research Assistant — Data Analytics & Mathematical Modelling

University of Bremen & German Aerospace Center (DLR)

Bremen, Germany

Prepared experimental flow and pressure sensor data using Python.
Validated data and explored patterns, inconsistencies, and simulation results.
Compared model outputs with observations and communicated findings with Matplotlib.

Education

Apr 2024 — Present

MSc Industrial Mathematics and Data Analysis

University of Bremen

Bremen, Germany

Focus on optimisation, statistics, scientific computing, numerical methods, and data analysis.

Education

Feb 2017 — Apr 2022

BSc Applied Mathematics

Noakhali Science and Technology University

Noakhali, Bangladesh

Foundation in applied mathematics, modelling, linear algebra, differential equations, and computation.

Technical toolkit

Programming & data

Python
Pandas
NumPy
SQL
R
Git

Mathematics

Optimisation
Statistics
Scientific computing
PDEs
Linear algebra

Machine learning

scikit-learn
XGBoost
Forecasting
Model validation
Time series

Visualisation

Plotly
Power BI
Matplotlib
Excel
Dashboarding

Selected projects

Case study in preparation

German Power Forecasting Dashboard

A visual workspace for comparing electricity forecasts, exploring errors, and understanding changing market conditions.

Python
Plotly
Energy

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Case study in preparation

Load Forecasting Library

Reusable modelling workflows for preparing load data, generating forecasts, and evaluating performance consistently.

Python
Time series
Validation

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Selected work

Solar & Wind Capacity Models

Data-driven profiles for validating and studying renewable generation across wind and solar portfolios.

Pandas
NumPy
Renewables

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Academic project

Online Algorithms Seminar

Academic work examining decision-making when inputs arrive sequentially and the future is unknown.

Algorithms
Optimisation
Research

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Academic project

Mathematical Epidemiology Model

A mathematical modelling study of epidemic dynamics, assumptions, and the interpretation of simulated outcomes.

Modelling
Differential equations
Python

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Live project

Portfolio Server Infrastructure

A lightweight static Astro site deployed on Oracle Cloud and served securely through Caddy.

Astro
Oracle Cloud
Caddy

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Research & presentations

Research interests

Energy systems, quantitative trading, online algorithms, stochastic modelling, forecasting, machine learning, PDEs, and data-driven optimisation.

Presentations & seminar work

Materials on online algorithms and applied mathematical modelling will be published here when they are ready to share.

Let’s connect

Interested in quantitative work, energy markets, or applied mathematics?

I’m always glad to exchange ideas and discuss thoughtful projects.

LocationBremen, Germany

LanguagesEnglish — C1German — A2, currently improvingBengali — Native

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