Simulation
If you like applying Simulation, every challenge here gives you a chance to practice it on a real industry brief.
- AnalysisAdvancedNew
Cache Coherence Protocol Comparison on a Multicore Simulator
Stand up gem5's Ruby coherence framework with both MESI and MOESI protocols on a 16-core configuration. Run the 6-benchmark suite (provided): producer-consumer queue, false-shar…
- Cache Coherence
- Multicore Architecture
- Simulation
Advanced Computer Architecture - AnalysisAdvancedNew
Design a Custom Page-Replacement Policy for a Tier-1 Cloud Provider Simulator
Use the provided simulator (Python harness wrapping a C++ page-cache model) and the team's 3 anonymized workload traces (web-cache, key-value store, batch analytics). Implement …
- Memory Management
- Page Replacement
- Benchmarking
Operating Systems - CodeAdvancedNew
Temporal Planner for a Robotics Mission Operator
You receive 30 days of mission logs with task lists, time windows, and actual durations. Encode the planning problem with temporal PDDL (PDDL 2.1 durative actions) and solve wit…
- Temporal Planning
- Pddl Modeling
- Simulation
Automated Planning - CodeAdvancedNew
Safety-Critical Test Harness for an AV Planner
Use CARLA (open-source AV simulator) and encode 10 representative safety scenarios across 3 categories (cut-in, pedestrian emergence, signalized-intersection right-of-way). Writ…
- Simulation
- Scenario Testing
- Safety Evaluation
AI for Autonomous Vehicles Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- DesignAdvancedNew
Design a Negotiation Protocol for Trading Agents
Choose a negotiation framework (alternating-offers Rubinstein, monotonic concession, or auction-based) and justify against the freight use case. Implement a simulator in Python …
- Agent Negotiation
- Game Theory
- Multi Agent Systems
Multi-Agent Systems - CodeAdvancedNew
Use Actor-Critic to Auto-Tune a HVAC Control Policy
You receive a Sinergym wrapper around the EnergyPlus model of one floor with 8 thermal zones, weather data for one year, and occupancy schedules. Train a Soft Actor-Critic (SAC,…
- Actor Critic
- Soft Actor Critic
- Continuous Control
Deep Reinforcement Learning - AnalysisAdvancedNew
Resilience Analysis of a National Power-Distribution Network
Receive an anonymized topology of the medium-voltage network (4,200 nodes, 4,800 edges, each edge with capacity + age + redundancy flag). Build the network in NetworkX, compute …
- Network Science
- Graph Analysis
- Resilience Analysis
Network Science and Computational Social Science - ResearchAdvancedNew
Planning Under Uncertainty for a Last-Mile Delivery Fleet
Build a simulator of the 50-block area with stochastic travel times conditioned on weather and time-of-day. Implement value iteration (for a small state space), MCTS (Monte Carl…
- Planning Under Uncertainty
- Markov Decision Processes
- Monte Carlo Tree Search
Automated Planning - Browse challenges
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Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- CodeAdvancedNew
Plan Inventory Replenishment as an MDP for an E-Commerce AI Startup
You receive 18 months of daily demand for 50 representative SKUs at one warehouse plus lead-time and unit-cost data. For one SKU at a time, formulate an MDP with state = (on-han…
- Mdp Modeling
- Value Iteration
- Dynamic Programming
Decision Making Under Uncertainty - CodeAdvancedNew
Run a Monte Carlo Tree Search Strategy for a Robotics Pick-and-Place Task
You receive a simulator of the pick-and-place task: a bin with 10 randomly-placed parts, an action space of which part to pick next, and a reward = parts picked per minute with …
- Monte Carlo Tree Search
- Planning
- Simulation
Decision Making Under Uncertainty - CodeAdvancedNew
Train a Deep Q-Network for Warehouse Robot Routing
You receive a Gymnasium-compatible warehouse simulator (50x50 grid, 8 dynamic obstacle pedestrians, 20 randomized pick locations) and a baseline A* planner script. Train a DQN a…
- Deep Q Learning
- Reinforcement Learning
- Pytorch
Deep Reinforcement Learning - CodeAdvancedNew
Prescriptive Route Optimization for a Sustainable Fashion Logistics Firm
Your team must develop a decision-support tool that recommends optimal delivery routes for EcoThreads' fleet. You'll need to model the logistics network, incorporate constraints…
- Optimization
- Simulation
- Route Planning
Business Analytics Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
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Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.