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User-friendly application for performing automated FEM parametric sweeps.

## GOAL
The goal is to enable engineers to configure parameter sweeps, automatically generate FEM cases, run them, extract results, store results, and estimate missing design points from collected data.

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## STAGES, TASKS, AND DELIVERABLES

### 1) FEM Automation Methodology
Automation for FEM case setup and post-processing.

#### Script 1: FEM Case Creation
- Import or load a 3D model.
- Apply material definitions and boundary conditions.
- Configure solver settings and simulation study.
- Produce a ready-to-run FEM project folder.

**Deliverables:**
- `script_setup.py`
- Example input file
- Example generated FEM folder
- Short instruction guide

#### Script 2: Result Extraction / Post-Processing
- Read FEM results and extract required parameters (inductance, flux linkage, losses, etc.).
- Output clean data files (CSV/JSON).
- Optional simple plots.

**Deliverables:**
- `script_postprocess.py`
- Example CSV/JSON output
- Short data format guide

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### 2) Framework for Templated 3D Model Generation
Script for generating parametric 3D geometry.

#### 3D Model Template Script
- Generate geometry from input parameters.
- Follow modeling/manufacturing constraints.
- Export compatible geometry for FEM setup.

**Deliverables:**
- `script_3d_template.py`
- Example generated 3D model
- Parameter description list

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### 3) UI Application for Automatic Parametric Sweeps
User-facing tool for managing sweep operations.

#### Sweep UI + Service
- UI for defining sweep ranges.
- Start / pause / resume simulation sequences.
- Monitor progress.
- Display basic results.

**Deliverables:**
- UI application (desktop or web)
- Sweep management backend/service
- Example sweep config file
- Setup instructions

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### 4) Simulation Data Storage & Retrieval Framework

#### Database + API
- Store simulation inputs, metadata, and outputs.
- Provide API for appending new entries.
- Support searching and retrieving past results.

**Deliverables:**
- Database schema
- `results_db` module or service
- API documentation
- Sample database entries

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### 5) Design Parameter Estimation (Interpolation)
Estimate values for unseen parameter sets.

#### Estimation / Interpolation Module
- Return predicted outputs using interpolation or simple regression.
- No complex ML required — only practical estimation.

**Deliverables:**
- `estimator.py`
- Example demo script
- Small comparison showing estimated vs known values

The task is straightforward: convert my incoming dental case data into a precise, print-ready STL framework using Exocad. I will supply everything you need—raw intra-oral STL files, full 3D models and any other supporting 3D data—so your focus can stay on accurate digital design rather than file prep.

Scope
• Selected design type: frameworks only. (If you also work with splints or monolithic restorations, mention it, as future cases may expand.)
• Deliverable: one clean, well-labeled STL that drops straight into CAM without additional repair.
• Turnaround: the faster the better; let me know your typical lead time up front.

Proposal expectations
A successful bid must include a detailed project proposal that walks me through your workflow in Exocad, quality-control steps, and estimated delivery schedule. Generic “I can do it” notes will be skipped.

I review every file thoroughly in print simulation software, so dimensional accuracy and smooth margins are critical acceptance criteria. Once approved, the project is complete; if tweaks are needed, please be ready for one fast revision round.

Share any relevant screenshots or examples of past Exocad framework cases inside your proposal and feel free to ask questions before starting—clear communication saves us both time.

Skills: CAD/CAM, 3D Rendering, Solidworks, Manufacturing Design, 3D Modelling, 3D Design, Digital Design, 3D Printing

📌 About the Project

We are developing Ori Cattle Tags, a rugged livestock tracking device with:

LoRaWAN Class A

IMU + Temperature sensors

Ultra-low-power MCU

Replaceable battery

Long-range pasture communication

Large-scale pilot tests (50,000+ head in pipeline)


Your role:
Ensure maximum LoRaWAN range, antenna tuning, and consistent communication across large ranch environments (1–10 km).


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🛠️ Your Responsibilities

1. Antenna Design & Tuning

Design OR tune PCB + external antennas for NA915 band

Optimize for rugged outdoor environments

Match impedance

Reduce packet loss


2. LoRaWAN RF Performance

Improve uplink reliability

Set correct spreading factors (SF7–SF12)

Adjust power levels pre-manufacture

Confirm link budgets


3. Field Simulation / Testing

Simulate cattle-mount position and signal absorption

Identify environmental blockers (trees, terrain, metal, humidity)

Prepare guidelines for optimal range


4. Manufacturing Prep

Provide required design specs for enclosure/templates

Tune final antenna after plastic enclosure is modeled

Work with hardware engineer for integration



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📦 Deliverables

Tuned antenna design (or modification to our existing design)

S11 / VSWR reports

NA915 compliance

Link budget analysis

Recommended gateway setup for ranches

Range test reports (bench + field simulation)

Documentation for manufacturing



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🔧 Required Experience

LoRaWAN (NA915)

RF design & tuning

PCB antenna design

Antenna matching

Semtech/Murata LoRa radios

Outdoor IoT devices

RF simulation software (HFSS, CST, etc.)

Range testing



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⏳ Timeline

4–8 weeks, milestone-based:

1. Antenna simulation & initial tuning


2. PCB modification recommendations


3. LoRaWAN performance testing


4. Environmental/rural range analysis


5. Manufacturing documentation




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💰 Compensation

$45/hr – $85/hr
Higher rate justified for real RF specialists.


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🐂 About Us

Ori is building an emotional-AI ecosystem across livestock, wearables, and education.
Our livestock division is expanding rapidly with large ranches (10k–30k head) onboarding for pilots.

Your work will directly impact thousands of devices deployed at scale.


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📬 To Apply

Please include:

LoRaWAN devices you've tuned

Antenna designs you've built or optimized

RF simulation examples

Range testing experience

Your availability

I have a working grid-based environment search-and-rescue drone path planner that combines A* with a reinforcement-learning layer. It already performs reasonably well, but I want it to outperform current state-of-the-art solutions in three concrete areas: increased efficiency (shorter mission time), better obstacle avoidance, and superior route optimisation. On top of that, the reinforcement-learning module must introduce a truly novel power-saving approach so that flight endurance is extended without compromising performance.

The existing code is clean and fully documented; you will receive the repo plus a small set of benchmark scenarios. Your mandate is to redesign the RL reward structure, refine or replace the current A* heuristics, and integrate any other innovative techniques you deem fit—multi-agent coordination, dynamic re-planning, hybrid meta-heuristics, etc.—as long as the final system:

• Clears every benchmark faster than the baseline while consuming less simulated energy
• Shows measurable gains in obstacle avoidance (fewer collisions or near misses)
• Produces routes with lower total cost than the baseline and at least one leading academic reference implementation

Provide the updated source, a short technical report with comparative plots, and a reproducible test script so I can verify the results locally. The project is self-contained; no field deployment is required at this stage, only simulation. If you have experience with RL libraries (e.g., TensorFlow, PyTorch), heuristic search tuning, or drone power modelling, I would love to see how you can push this algorithm beyond current limits.

Skills: Python, Test Automation, Machine Learning (ML), Robotics, Software Development, Artificial Intelligence, Data Visualization, Simulation, Software Engineering, Reinforcement Learning