Applied ML Engineer (PINN + Time-Series) — Water Quality Prediction + Unit Sizing Prototype

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@datahub0

Hi, I’m a data scientist / ML engineer with hands-on experience building time-series models on messy industrial data and physics-informed ML (PINNs) that generalize to unseen sites. This project closely matches my recent work. 1) Physics-Informed ML Experience I build hybrid PINN-style models where domain knowledge acts as soft guardrails, not rigid PDE solvers. Typical setup: Data-driven backbone (LSTM / Temporal CNN / Neural ODE) Physics constraints (bounds, monotonicity, mass-balance–style penalties) added to loss Explicit comparison vs strong baselines on held-out sites Focus is on generalization and explainability, not academic overfitting. 2) PDF Lab Reports → Clean Time-Series pdfplumber for text + tables; Camelot/Tabula only where reliable Parameter & unit normalization (DO, ORP, BOD, COD, turbidity, etc.) Monthly site-level time indexing Clear tagging: pre- vs post-installation Output: reusable CSV/Parquet schema + data dictionary All assumptions and gaps are explicitly logged. 3) Preventing Overfitting Across Sites Train/validation split strictly by site Global models with site-invariant features (+ optional embeddings) Leakage detection via strong baselines Early stopping on unseen sites + targeted failure analysis 4) Timeline (Phase 1) 2 weeks total (pipeline → baseline → hybrid PINN → unit sizing + docs) I focus on working commercial prototypes, clean Python code, and explainable outputs for non-technical teams.

@chiragardeshna

Hi there, This project aligns with work I have already delivered in physics-constrained forecasting and messy industrial time-series pipelines. I recently supported an applied engineering team by turning PDF lab reports into site-level time series and delivering a prototype model that generalized across unseen assets. My experience covers PDF extraction, unit normalization, time-series modeling, and PINN-style constraints using Python, PyTorch, pandas, scikit-learn, and pdfplumber. I handle end-to-end delivery, including data validation, model benchmarking, and a clean handoff with documented assumptions and limits. I reduce risk by enforcing site-level train/test splits, simple physics guardrails, and explicit uncertainty reporting instead of overfitting complex models. I can start immediately and stay accountable through completion. Regards Chirag

@AhmadXpert2410

Hi there, I’m excited to help build your water quality prototype and unit sizing engine. I specialize in time-series forecasting on messy real-world datasets and have hands-on experience with Physics-Informed Neural Networks (PINNs). I’ll extract data from PDF lab reports using pdfplumber/Camelot, normalize units (DO, BOD, COD, turbidity, etc.), and generate clean time-series datasets per site/month, tagged before vs after installation. I’ll build a baseline PyTorch model and a hybrid PINN to generalize across sites, with uncertainty estimates and explainable outputs. Predictions will translate into recommended unit counts, packaged in clean Python code with a plain-English summary for stakeholders. I prevent overfitting by splitting data by site, applying regularization, and leveraging physics constraints. Estimated Phase 1 delivery: 3-4 weeks, fixed price 35k inr. I’ve implemented PINNs for environmental systems, improving generalization while respecting real-world physics. I can deliver a working, explainable prototype that meets all success criteria and provides a repeatable pipeline for future data. Regards, Ahmad

@mohd25naseem

As a qualified and experienced data scientist, I would be a perfect fit for your Applied ML Engineering project. Over the years, I've successfully handled various projects similar to yours, applying machine learning techniques to real-world datasets, just as you require. From my portfolio, you'll find compelling evidence of my competency with Physics-Informed Machine Learning (PINN) algorithms; I've built multiple robust models justifying its immense strength in solving complex problems. Overfitting is always a concern when dealing with multiple sites. Drawing from my extensive experience with validation paradigms in predictive modeling - specifically a focus on train/test split by site instead of random rows - mitigating against such risks will come as second nature to me. Foreseeing potential issues within core phases of your project like minefield dataset, assumptions documentation, and generalized model application are areas we both appreciate. Throughout Phase 1, rest assured you'll be working with a proactive professional dedicated to transparency and accountability throughout every stage of the process.

@abdf2010

Hello there, I reviewed your project Applied ML Engineer (PINN + Time-Series) — Water Quality Prediction + Unit Sizing Prototype and understood the requirements at a high level. I focus on delivering clear, stable, and maintainable solutions aligned with the actual scope, I can work with Python, PDF, Machine Learning (ML) and follow a clean development process with proper structure and error handling. If this aligns with what you’re looking for, please come to chat to discuss further. Best regards

@malkesh3m

⭐ Hello there, My availability is immediate. I read your project post on Python AI/ML Developer for Water Quality Prediction + Unit Sizing Prototype. We are experienced full-stack Python developers with skill sets in: Python, Django, Flask, FastAPI, Jupyter Notebook, Selenium, Data Visualization, ETL AI/ML & Data Science: Model development, training & deployment, NLP, Computer Vision, Predictive Analytics, Deep Learning React, JavaScript, jQuery, TypeScript, NextJS, React Native NodeJS, ExpressJS Web App Development, Web/API Scraping API Development, Authentication, Authorization SQLAlchemy, PostgresDB, MySQL, SQLite, SQLServer, Datasets Web hosting, Docker, Azure, AWS, GCP, Digital Ocean, GoDaddy, Web Hosting Python Libraries: NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc. Please send a message so we can quickly discuss your project and proceed further. I am looking forward to hearing from you. Thanks

@Madnikpvtltd

I understand this is a commercial prototype, not a research exercise. I can build a complete Phase-1 pipeline: extracting messy PDF lab reports into clean site-wise time-series data, developing a robust baseline model, and implementing a physics-informed hybrid model that generalizes to unseen sites. My focus will be on site-level validation (no data leakage), explainable predictions, and a practical **unit-sizing recommendation engine with uncertainty handling. I’ll deliver a clean Python/PyTorch codebase, documented assumptions, and a plain-English summary for stakeholders. Estimated timeline: 7 days Budget we can discuss. Happy to discuss prior PINN-style work and implementation details.

@bhuwanbhupi35

Hello Dear Client, i am not pro but i have good command in python and i have done various ML data analysis using python and my last project was also through python analysis and things like that py type files so that i can do this i think if i got the clear details and i am also flexible with budget than others if you are interested inbox me

@dushyant317

Hello, I’ve carefully reviewed your project requirements and clearly understand the tasks involved. I have 13 years of experience and strong expertise in the exact skills this project requires. I have successfully delivered similar projects before and can share relevant samples if needed. I will complete this within your expected timeline while maintaining quality and clear communication. I look forward to working with you and contributing sincerely to your project’s success.

@vishala81

Hi, thanks for sharing the details. I’ve worked with Tesseract OCR on document images where strong preprocessing is critical, especially for non-Latin scripts like Devanagari. Extracting handwritten text from photographs (not scans) requires careful handling of noise, lighting variations, skew, and background artifacts, which is where most OCR pipelines fail. For this task, I would focus first on image preprocessing using techniques like grayscale conversion, adaptive thresholding, denoising, contrast enhancement, and region-of-interest cropping to improve handwritten text recognition in Marathi. I’ve used Tesseract’s Devanagari language models and tuned configurations to get more stable results on real-world photos rather than clean documents. The extracted fields will be structured cleanly and exported to CSV format, with consistent column mapping so the output is easy to review and process further. I also make sure the pipeline is repeatable, so additional ration card images can be processed the same way without manual intervention. I can share sample outputs and screenshots from similar OCR work (handwritten and regional-language documents) during our discussion. Happy to review a few sample images first to confirm accuracy expectations before proceeding.

@Ankurhardia34

Hello, I’m Ankur, a freelance developer with a dedicated team of professionals. I read all your requirements for website and I assure you that I will provide high-quality work at the proper time. Additionally, we also provide you 3 months of support from our side. As a Full Stack Developer, I specialize in Web and App Development, boasting a portfolio of stunning projects with top-notch UI/UX design. My expertise spans Flutter (for both Android and iOS), PHP, and WordPress, and I bring over 7 years of experience to the table. Whether it’s websites, applications, or e-commerce platforms, I’ve got you covered. But I’m not limited to just coding. My skill set extends to graphic design and logo creation, offering you a one-stop solution for all your project needs. With a track record of over 500 completed projects, I am committed to delivering nothing short of excellence. My ultimate goal is your complete satisfaction. Thank you for considering me for your project. I’m ready to transform your vision into a reality that stands out in today’s competitive landscape. Best Regards, Ankur Hardiya

@kishanbwts

Hello, I have delivered physics-informed ML solutions, including a PINN model for fluid dynamics that incorporated physical constraints as soft guardrails to improve generalization beyond training sites. I can share detailed documentation from that project on request. For PDF lab reports, I use pdfplumber to extract text and tables, combined with Camelot for tabular data. I write robust parsers to normalize units and parameters, handling inconsistencies by cross-validating multiple extraction methods and building custom dictionaries for parameter mapping. To avoid overfitting across sites, I design train/test splits by site, not by random rows, and use regularization plus early stopping. I validate performance on held-out sites and compare physics-informed and baseline models to ensure robustness. Phase 1 would take 4-5 weeks, including data pipeline, baseline and PINN model, unit sizing logic, documentation, and validation. My fixed price for the prototype is $7,500. I look forward to discussing specific dataset details and physics constraint assumptions to deliver a practical, explainable unit sizing engine.

@ShivomAISolution

I’ve reviewed your requirements and clearly understand this is a commercial Phase-1 prototype, not research focused on turning messy PDF lab data into a generalizable, explainable unit-sizing engine for nanobubble water treatment. Main focus areas: • PDF lab report extraction → clean time-series datasets • Site-aware normalization, tagging (pre/post installation) • Baseline + physics-informed (PINN-style) modeling • Cross-site generalization & validation (hold-out sites) • Explainable unit-sizing recommendations with uncertainty Process flow: Quick chat / call → PDF data audit → Extraction & schema design → Baseline model → PINN / hybrid constraints → Unit sizing logic → Validation & documentation → Handover I’ve worked on messy real-world time-series problems and physics-informed ML systems, building reproducible pipelines, guarding against site leakage, and documenting assumptions and failure modes for non-technical stakeholders. Phase-1 can be completed in 2–4 weeks, with a fixed-price prototype and clean handover suitable for future extensions. I focus on working models, clear validation, and decision-ready outputs happy to share PINN examples and walk through my approach on a quick call.

@prathmeshj202

Hello, I’m a Python/ML engineer experienced in time-series modeling, messy real-world data pipelines, and physics-informed ML systems. I understand this is a commercial prototype, not a research experiment, and my focus will be on building a working, explainable, and generalizable solution. 1) PINN / Physics-Informed Experience I’ve built hybrid ML models combining data-driven predictions with physical constraints (bounded dynamics, conservation rules, monotonic trends). These models improved generalization on unseen environments and prevented unrealistic predictions. 2) PDF - Clean Dataset Approach I’ll build a robust ETL pipeline using pdfplumber + Camelot to extract lab parameters (DO, ORP, BOD, COD, turbidity, etc.), normalize units, tag before/after installation, and produce structured time-series datasets with a reusable schema and data dictionary. 3) Preventing Overfitting Across Sites Train/test split by site (not random rows) Leave-one-site-out validation Regularization + physics-based guardrails Cross-site performance reporting 4) Timeline Phase 1 delivery in 2 weeks.

@ashishrai7704080

With a proven track record in Python and a wide range of relevant skills, I am confident in delivering the robust and efficient prototype you seek. In previous projects, I have leveraged Physics-Informed ML or PINN to solve scientific problems that mirrored your real-world challenge. Specifically, I tackled a computational fluid dynamics problem by building and deploying a custom PINN model using PyTorch, which lead to precise and reliable predictions. This expertise would translate excellently to your task as careful consideration of variables is required for both PINN and nanobubble water treatment units. Regarding the data pipeline from the PDF lab reports, my proficiency with python library 'pdfplumber' coupled with my meticulous approach to handling data will allow for accurate extraction, normalization, and creation of structured time-series datasets following a clear schema. Prioritizing transparency, I would extensively document every assumption made in the cleaning process and maintain my documentation accordingly as new data feeds into the pipeline. Minimizing overfitting across sites is key in any generalized model's success. I propose an innovative train/test split strategy – not at random rows, but based on site locations – to ensure site-specific information doesn't bleed into other sites' validations. As for the deliverables you required within Phase

@robink0301

Hello, I’m a senior ML engineer with real, shipped experience in PINNs, physics‑informed forecasting, and messy time‑series pipelines. This project is exactly in my wheelhouse. 1. PINN Experience Built PINN models for diffusion–reaction and hydrodynamic systems using PyTorch + DeepXDE, outperforming data‑only baselines on unseen locations. 2. PDF → Dataset Approach pdfplumber + Camelot → unit normalization → site/month time‑series → install-date tagging → clean CSV/Parquet schema. 3. Preventing Overfitting Strict site‑level splits, physics constraints as priors, regularization, and cross‑site residual checks. 4. Timeline & Price for Phase1 4–6weeks/$300USD (final after sample PDFs). I build working prototypes, not academic demos. Ready to start immediately.

@zaheermoe

Hi there, I'm Zaheer Mahomed and I'm excited about the opportunity to work on your Phase 1 Prototype project focused on predicting water quality changes post-installation of nanobubble water treatment units. I have a strong background in time-series forecasting on real-world datasets, hands-on experience with Physics-Informed ML/PINNs, and expertise in Python engineering for clean, modular code. One strategy I propose is to leverage my experience in handling incomplete and inconsistent reports by implementing a robust data normalization process to ensure accurate dataset creation. I am available to discuss further and look forward to contributing to your project's success. Thank you for your time and consideration. Best, Zaheer Mahomed